library(quantmod)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Loading required package: TTR
## Version 0.4-0 included new data defaults. See ?getSymbols.
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
startDate = as.Date("2010-01-01")
endDate = as.Date("2015-12-31")
startDate = as.Date("2010-01-01")
endDate = as.Date("2015-12-31")
getSymbols("DJIA", src = "yahoo", from = startDate, to = endDate)
## As of 0.4-0, 'getSymbols' uses env=parent.frame() and
## auto.assign=TRUE by default.
##
## This behavior will be phased out in 0.5-0 when the call will
## default to use auto.assign=FALSE. getOption("getSymbols.env") and
## getOptions("getSymbols.auto.assign") are now checked for alternate defaults
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for more details.
## [1] "DJIA"
RSI3<-RSI(Op(DJIA), n= 3)
#Calculate a 3-period relative strength index (RSI) off the open price
EMA5<-EMA(Op(DJIA),n=5)
#Calculate a 5-period exponential moving average (EMA)
EMAcross<- Op(DJIA)-EMA5
#Let’s explore the difference between the open price and our 5-period EMA
DEMA10<-DEMA(Cl(DJIA),n = 10, v = 1, wilder = FALSE)
DEMA10c<-Cl(DJIA) - DEMA10
MACD<-MACD(Op(DJIA),fast = 12, slow = 26, signal = 9)
#Calculate a MACD with standard parameters
MACDsignal<-MACD[,2]
#Grab just the signal line to use as our indicator.
SMI<-SMI(Op(DJIA),n=13,slow=25,fast=2,signal=9)
#Stochastic Oscillator with standard parameters
SMI<-SMI[,1]
#Grab just the oscillator to use as our indicator
BB<-BBands(Op(DJIA),n=20,sd=2)
BBp<-BB[,4]
CCI20<-CCI(DJIA[,3:5],n=20)
#A 20-period Commodity Channel Index calculated of the High/Low/Close of our data
# Return sign creation
ClosingPrice<-Cl(DJIA)
Trend<-diff(ClosingPrice, lag = 1, differences = 1, arithmetic = TRUE, log = FALSE, na.pad = TRUE)
#Calculate the difference between the close price at T and close price T-1
Class<-ifelse(Trend>0,"UP","DOWN")
#Create a binary classification variable, the variable we are trying to predict.
DJIADF<-data.frame(date = index(DJIA),DJIA, row.names=NULL)
library(wikipediatrend)
views1<-wp_trend(page ="The Home Depot" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/The_Home%20Depot
## http://stats.grok.se/json/en/201002/The_Home%20Depot
## http://stats.grok.se/json/en/201003/The_Home%20Depot
## http://stats.grok.se/json/en/201004/The_Home%20Depot
## http://stats.grok.se/json/en/201005/The_Home%20Depot
## http://stats.grok.se/json/en/201006/The_Home%20Depot
## http://stats.grok.se/json/en/201007/The_Home%20Depot
## http://stats.grok.se/json/en/201008/The_Home%20Depot
## http://stats.grok.se/json/en/201009/The_Home%20Depot
## http://stats.grok.se/json/en/201010/The_Home%20Depot
## http://stats.grok.se/json/en/201011/The_Home%20Depot
## http://stats.grok.se/json/en/201012/The_Home%20Depot
## http://stats.grok.se/json/en/201101/The_Home%20Depot
## http://stats.grok.se/json/en/201102/The_Home%20Depot
## http://stats.grok.se/json/en/201103/The_Home%20Depot
## http://stats.grok.se/json/en/201104/The_Home%20Depot
## http://stats.grok.se/json/en/201105/The_Home%20Depot
## http://stats.grok.se/json/en/201106/The_Home%20Depot
## http://stats.grok.se/json/en/201107/The_Home%20Depot
## http://stats.grok.se/json/en/201108/The_Home%20Depot
## http://stats.grok.se/json/en/201109/The_Home%20Depot
## http://stats.grok.se/json/en/201110/The_Home%20Depot
## http://stats.grok.se/json/en/201111/The_Home%20Depot
## http://stats.grok.se/json/en/201112/The_Home%20Depot
## http://stats.grok.se/json/en/201201/The_Home%20Depot
## http://stats.grok.se/json/en/201202/The_Home%20Depot
## http://stats.grok.se/json/en/201203/The_Home%20Depot
## http://stats.grok.se/json/en/201204/The_Home%20Depot
## http://stats.grok.se/json/en/201205/The_Home%20Depot
## http://stats.grok.se/json/en/201206/The_Home%20Depot
## http://stats.grok.se/json/en/201207/The_Home%20Depot
## http://stats.grok.se/json/en/201208/The_Home%20Depot
## http://stats.grok.se/json/en/201209/The_Home%20Depot
## http://stats.grok.se/json/en/201210/The_Home%20Depot
## http://stats.grok.se/json/en/201211/The_Home%20Depot
## http://stats.grok.se/json/en/201212/The_Home%20Depot
## http://stats.grok.se/json/en/201301/The_Home%20Depot
## http://stats.grok.se/json/en/201302/The_Home%20Depot
## http://stats.grok.se/json/en/201303/The_Home%20Depot
## http://stats.grok.se/json/en/201304/The_Home%20Depot
## http://stats.grok.se/json/en/201305/The_Home%20Depot
## http://stats.grok.se/json/en/201306/The_Home%20Depot
## http://stats.grok.se/json/en/201307/The_Home%20Depot
## http://stats.grok.se/json/en/201308/The_Home%20Depot
## http://stats.grok.se/json/en/201309/The_Home%20Depot
## http://stats.grok.se/json/en/201310/The_Home%20Depot
## http://stats.grok.se/json/en/201311/The_Home%20Depot
## http://stats.grok.se/json/en/201312/The_Home%20Depot
## http://stats.grok.se/json/en/201401/The_Home%20Depot
## http://stats.grok.se/json/en/201402/The_Home%20Depot
## http://stats.grok.se/json/en/201403/The_Home%20Depot
## http://stats.grok.se/json/en/201404/The_Home%20Depot
## http://stats.grok.se/json/en/201405/The_Home%20Depot
## http://stats.grok.se/json/en/201406/The_Home%20Depot
## http://stats.grok.se/json/en/201407/The_Home%20Depot
## http://stats.grok.se/json/en/201408/The_Home%20Depot
## http://stats.grok.se/json/en/201409/The_Home%20Depot
## http://stats.grok.se/json/en/201410/The_Home%20Depot
## http://stats.grok.se/json/en/201411/The_Home%20Depot
## http://stats.grok.se/json/en/201412/The_Home%20Depot
## http://stats.grok.se/json/en/201501/The_Home%20Depot
## http://stats.grok.se/json/en/201502/The_Home%20Depot
## http://stats.grok.se/json/en/201503/The_Home%20Depot
## http://stats.grok.se/json/en/201504/The_Home%20Depot
## http://stats.grok.se/json/en/201505/The_Home%20Depot
## http://stats.grok.se/json/en/201506/The_Home%20Depot
## http://stats.grok.se/json/en/201507/The_Home%20Depot
## http://stats.grok.se/json/en/201508/The_Home%20Depot
## http://stats.grok.se/json/en/201509/The_Home%20Depot
## http://stats.grok.se/json/en/201510/The_Home%20Depot
## http://stats.grok.se/json/en/201511/The_Home%20Depot
## http://stats.grok.se/json/en/201512/The_Home%20Depot
views2<-wp_trend(page ="ExxonMobil" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/ExxonMobil
## http://stats.grok.se/json/en/201002/ExxonMobil
## http://stats.grok.se/json/en/201003/ExxonMobil
## http://stats.grok.se/json/en/201004/ExxonMobil
## http://stats.grok.se/json/en/201005/ExxonMobil
## http://stats.grok.se/json/en/201006/ExxonMobil
## http://stats.grok.se/json/en/201007/ExxonMobil
## http://stats.grok.se/json/en/201008/ExxonMobil
## http://stats.grok.se/json/en/201009/ExxonMobil
## http://stats.grok.se/json/en/201010/ExxonMobil
## http://stats.grok.se/json/en/201011/ExxonMobil
## http://stats.grok.se/json/en/201012/ExxonMobil
## http://stats.grok.se/json/en/201101/ExxonMobil
## http://stats.grok.se/json/en/201102/ExxonMobil
## http://stats.grok.se/json/en/201103/ExxonMobil
## http://stats.grok.se/json/en/201104/ExxonMobil
## http://stats.grok.se/json/en/201105/ExxonMobil
## http://stats.grok.se/json/en/201106/ExxonMobil
## http://stats.grok.se/json/en/201107/ExxonMobil
## http://stats.grok.se/json/en/201108/ExxonMobil
## http://stats.grok.se/json/en/201109/ExxonMobil
## http://stats.grok.se/json/en/201110/ExxonMobil
## http://stats.grok.se/json/en/201111/ExxonMobil
## http://stats.grok.se/json/en/201112/ExxonMobil
## http://stats.grok.se/json/en/201201/ExxonMobil
## http://stats.grok.se/json/en/201202/ExxonMobil
## http://stats.grok.se/json/en/201203/ExxonMobil
## http://stats.grok.se/json/en/201204/ExxonMobil
## http://stats.grok.se/json/en/201205/ExxonMobil
## http://stats.grok.se/json/en/201206/ExxonMobil
## http://stats.grok.se/json/en/201207/ExxonMobil
## http://stats.grok.se/json/en/201208/ExxonMobil
## http://stats.grok.se/json/en/201209/ExxonMobil
## http://stats.grok.se/json/en/201210/ExxonMobil
## http://stats.grok.se/json/en/201211/ExxonMobil
## http://stats.grok.se/json/en/201212/ExxonMobil
## http://stats.grok.se/json/en/201301/ExxonMobil
## http://stats.grok.se/json/en/201302/ExxonMobil
## http://stats.grok.se/json/en/201303/ExxonMobil
## http://stats.grok.se/json/en/201304/ExxonMobil
## http://stats.grok.se/json/en/201305/ExxonMobil
## http://stats.grok.se/json/en/201306/ExxonMobil
## http://stats.grok.se/json/en/201307/ExxonMobil
## http://stats.grok.se/json/en/201308/ExxonMobil
## http://stats.grok.se/json/en/201309/ExxonMobil
## http://stats.grok.se/json/en/201310/ExxonMobil
## http://stats.grok.se/json/en/201311/ExxonMobil
## http://stats.grok.se/json/en/201312/ExxonMobil
## http://stats.grok.se/json/en/201401/ExxonMobil
## http://stats.grok.se/json/en/201402/ExxonMobil
## http://stats.grok.se/json/en/201403/ExxonMobil
## http://stats.grok.se/json/en/201404/ExxonMobil
## http://stats.grok.se/json/en/201405/ExxonMobil
## http://stats.grok.se/json/en/201406/ExxonMobil
## http://stats.grok.se/json/en/201407/ExxonMobil
## http://stats.grok.se/json/en/201408/ExxonMobil
## http://stats.grok.se/json/en/201409/ExxonMobil
## http://stats.grok.se/json/en/201410/ExxonMobil
## http://stats.grok.se/json/en/201411/ExxonMobil
## http://stats.grok.se/json/en/201412/ExxonMobil
## http://stats.grok.se/json/en/201501/ExxonMobil
## http://stats.grok.se/json/en/201502/ExxonMobil
## http://stats.grok.se/json/en/201503/ExxonMobil
## http://stats.grok.se/json/en/201504/ExxonMobil
## http://stats.grok.se/json/en/201505/ExxonMobil
## http://stats.grok.se/json/en/201506/ExxonMobil
## http://stats.grok.se/json/en/201507/ExxonMobil
## http://stats.grok.se/json/en/201508/ExxonMobil
## http://stats.grok.se/json/en/201509/ExxonMobil
## http://stats.grok.se/json/en/201510/ExxonMobil
## http://stats.grok.se/json/en/201511/ExxonMobil
## http://stats.grok.se/json/en/201512/ExxonMobil
views3<-wp_trend(page ="DuPont" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/DuPont
## http://stats.grok.se/json/en/201002/DuPont
## http://stats.grok.se/json/en/201003/DuPont
## http://stats.grok.se/json/en/201004/DuPont
## http://stats.grok.se/json/en/201005/DuPont
## http://stats.grok.se/json/en/201006/DuPont
## http://stats.grok.se/json/en/201007/DuPont
## http://stats.grok.se/json/en/201008/DuPont
## http://stats.grok.se/json/en/201009/DuPont
## http://stats.grok.se/json/en/201010/DuPont
## http://stats.grok.se/json/en/201011/DuPont
## http://stats.grok.se/json/en/201012/DuPont
## http://stats.grok.se/json/en/201101/DuPont
## http://stats.grok.se/json/en/201102/DuPont
## http://stats.grok.se/json/en/201103/DuPont
## http://stats.grok.se/json/en/201104/DuPont
## http://stats.grok.se/json/en/201105/DuPont
## http://stats.grok.se/json/en/201106/DuPont
## http://stats.grok.se/json/en/201107/DuPont
## http://stats.grok.se/json/en/201108/DuPont
## http://stats.grok.se/json/en/201109/DuPont
## http://stats.grok.se/json/en/201110/DuPont
## http://stats.grok.se/json/en/201111/DuPont
## http://stats.grok.se/json/en/201112/DuPont
## http://stats.grok.se/json/en/201201/DuPont
## http://stats.grok.se/json/en/201202/DuPont
## http://stats.grok.se/json/en/201203/DuPont
## http://stats.grok.se/json/en/201204/DuPont
## http://stats.grok.se/json/en/201205/DuPont
## http://stats.grok.se/json/en/201206/DuPont
## http://stats.grok.se/json/en/201207/DuPont
## http://stats.grok.se/json/en/201208/DuPont
## http://stats.grok.se/json/en/201209/DuPont
## http://stats.grok.se/json/en/201210/DuPont
## http://stats.grok.se/json/en/201211/DuPont
## http://stats.grok.se/json/en/201212/DuPont
## http://stats.grok.se/json/en/201301/DuPont
## http://stats.grok.se/json/en/201302/DuPont
## http://stats.grok.se/json/en/201303/DuPont
## http://stats.grok.se/json/en/201304/DuPont
## http://stats.grok.se/json/en/201305/DuPont
## http://stats.grok.se/json/en/201306/DuPont
## http://stats.grok.se/json/en/201307/DuPont
## http://stats.grok.se/json/en/201308/DuPont
## http://stats.grok.se/json/en/201309/DuPont
## http://stats.grok.se/json/en/201310/DuPont
## http://stats.grok.se/json/en/201311/DuPont
## http://stats.grok.se/json/en/201312/DuPont
## http://stats.grok.se/json/en/201401/DuPont
## http://stats.grok.se/json/en/201402/DuPont
## http://stats.grok.se/json/en/201403/DuPont
## http://stats.grok.se/json/en/201404/DuPont
## http://stats.grok.se/json/en/201405/DuPont
## http://stats.grok.se/json/en/201406/DuPont
## http://stats.grok.se/json/en/201407/DuPont
## http://stats.grok.se/json/en/201408/DuPont
## http://stats.grok.se/json/en/201409/DuPont
## http://stats.grok.se/json/en/201410/DuPont
## http://stats.grok.se/json/en/201411/DuPont
## http://stats.grok.se/json/en/201412/DuPont
## http://stats.grok.se/json/en/201501/DuPont
## http://stats.grok.se/json/en/201502/DuPont
## http://stats.grok.se/json/en/201503/DuPont
## http://stats.grok.se/json/en/201504/DuPont
## http://stats.grok.se/json/en/201505/DuPont
## http://stats.grok.se/json/en/201506/DuPont
## http://stats.grok.se/json/en/201507/DuPont
## http://stats.grok.se/json/en/201508/DuPont
## http://stats.grok.se/json/en/201509/DuPont
## http://stats.grok.se/json/en/201510/DuPont
## http://stats.grok.se/json/en/201511/DuPont
## http://stats.grok.se/json/en/201512/DuPont
views4<-wp_trend(page ="3M" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/3M
## http://stats.grok.se/json/en/201002/3M
## http://stats.grok.se/json/en/201003/3M
## http://stats.grok.se/json/en/201004/3M
## http://stats.grok.se/json/en/201005/3M
## http://stats.grok.se/json/en/201006/3M
## http://stats.grok.se/json/en/201007/3M
## http://stats.grok.se/json/en/201008/3M
## http://stats.grok.se/json/en/201009/3M
## http://stats.grok.se/json/en/201010/3M
## http://stats.grok.se/json/en/201011/3M
## http://stats.grok.se/json/en/201012/3M
## http://stats.grok.se/json/en/201101/3M
## http://stats.grok.se/json/en/201102/3M
## http://stats.grok.se/json/en/201103/3M
## http://stats.grok.se/json/en/201104/3M
## http://stats.grok.se/json/en/201105/3M
## http://stats.grok.se/json/en/201106/3M
## http://stats.grok.se/json/en/201107/3M
## http://stats.grok.se/json/en/201108/3M
## http://stats.grok.se/json/en/201109/3M
## http://stats.grok.se/json/en/201110/3M
## http://stats.grok.se/json/en/201111/3M
## http://stats.grok.se/json/en/201112/3M
## http://stats.grok.se/json/en/201201/3M
## http://stats.grok.se/json/en/201202/3M
## http://stats.grok.se/json/en/201203/3M
## http://stats.grok.se/json/en/201204/3M
## http://stats.grok.se/json/en/201205/3M
## http://stats.grok.se/json/en/201206/3M
## http://stats.grok.se/json/en/201207/3M
## http://stats.grok.se/json/en/201208/3M
## http://stats.grok.se/json/en/201209/3M
## http://stats.grok.se/json/en/201210/3M
## http://stats.grok.se/json/en/201211/3M
## http://stats.grok.se/json/en/201212/3M
## http://stats.grok.se/json/en/201301/3M
## http://stats.grok.se/json/en/201302/3M
## http://stats.grok.se/json/en/201303/3M
## http://stats.grok.se/json/en/201304/3M
## http://stats.grok.se/json/en/201305/3M
## http://stats.grok.se/json/en/201306/3M
## http://stats.grok.se/json/en/201307/3M
## http://stats.grok.se/json/en/201308/3M
## http://stats.grok.se/json/en/201309/3M
## http://stats.grok.se/json/en/201310/3M
## http://stats.grok.se/json/en/201311/3M
## http://stats.grok.se/json/en/201312/3M
## http://stats.grok.se/json/en/201401/3M
## http://stats.grok.se/json/en/201402/3M
## http://stats.grok.se/json/en/201403/3M
## http://stats.grok.se/json/en/201404/3M
## http://stats.grok.se/json/en/201405/3M
## http://stats.grok.se/json/en/201406/3M
## http://stats.grok.se/json/en/201407/3M
## http://stats.grok.se/json/en/201408/3M
## http://stats.grok.se/json/en/201409/3M
## http://stats.grok.se/json/en/201410/3M
## http://stats.grok.se/json/en/201411/3M
## http://stats.grok.se/json/en/201412/3M
## http://stats.grok.se/json/en/201501/3M
## http://stats.grok.se/json/en/201502/3M
## http://stats.grok.se/json/en/201503/3M
## http://stats.grok.se/json/en/201504/3M
## http://stats.grok.se/json/en/201505/3M
## http://stats.grok.se/json/en/201506/3M
## http://stats.grok.se/json/en/201507/3M
## http://stats.grok.se/json/en/201508/3M
## http://stats.grok.se/json/en/201509/3M
## http://stats.grok.se/json/en/201510/3M
## http://stats.grok.se/json/en/201511/3M
## http://stats.grok.se/json/en/201512/3M
views6<-wp_trend(page ="Procter & Gamble" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/Procter_&%20Gamble
## http://stats.grok.se/json/en/201002/Procter_&%20Gamble
## http://stats.grok.se/json/en/201003/Procter_&%20Gamble
## http://stats.grok.se/json/en/201004/Procter_&%20Gamble
## http://stats.grok.se/json/en/201005/Procter_&%20Gamble
## http://stats.grok.se/json/en/201006/Procter_&%20Gamble
## http://stats.grok.se/json/en/201007/Procter_&%20Gamble
## http://stats.grok.se/json/en/201008/Procter_&%20Gamble
## http://stats.grok.se/json/en/201009/Procter_&%20Gamble
## http://stats.grok.se/json/en/201010/Procter_&%20Gamble
## http://stats.grok.se/json/en/201011/Procter_&%20Gamble
## http://stats.grok.se/json/en/201012/Procter_&%20Gamble
## http://stats.grok.se/json/en/201101/Procter_&%20Gamble
## http://stats.grok.se/json/en/201102/Procter_&%20Gamble
## http://stats.grok.se/json/en/201103/Procter_&%20Gamble
## http://stats.grok.se/json/en/201104/Procter_&%20Gamble
## http://stats.grok.se/json/en/201105/Procter_&%20Gamble
## http://stats.grok.se/json/en/201106/Procter_&%20Gamble
## http://stats.grok.se/json/en/201107/Procter_&%20Gamble
## http://stats.grok.se/json/en/201108/Procter_&%20Gamble
## http://stats.grok.se/json/en/201109/Procter_&%20Gamble
## http://stats.grok.se/json/en/201110/Procter_&%20Gamble
## http://stats.grok.se/json/en/201111/Procter_&%20Gamble
## http://stats.grok.se/json/en/201112/Procter_&%20Gamble
## http://stats.grok.se/json/en/201201/Procter_&%20Gamble
## http://stats.grok.se/json/en/201202/Procter_&%20Gamble
## http://stats.grok.se/json/en/201203/Procter_&%20Gamble
## http://stats.grok.se/json/en/201204/Procter_&%20Gamble
## http://stats.grok.se/json/en/201205/Procter_&%20Gamble
## http://stats.grok.se/json/en/201206/Procter_&%20Gamble
## http://stats.grok.se/json/en/201207/Procter_&%20Gamble
## http://stats.grok.se/json/en/201208/Procter_&%20Gamble
## http://stats.grok.se/json/en/201209/Procter_&%20Gamble
## http://stats.grok.se/json/en/201210/Procter_&%20Gamble
## http://stats.grok.se/json/en/201211/Procter_&%20Gamble
## http://stats.grok.se/json/en/201212/Procter_&%20Gamble
## http://stats.grok.se/json/en/201301/Procter_&%20Gamble
## http://stats.grok.se/json/en/201302/Procter_&%20Gamble
## http://stats.grok.se/json/en/201303/Procter_&%20Gamble
## http://stats.grok.se/json/en/201304/Procter_&%20Gamble
## http://stats.grok.se/json/en/201305/Procter_&%20Gamble
## http://stats.grok.se/json/en/201306/Procter_&%20Gamble
## http://stats.grok.se/json/en/201307/Procter_&%20Gamble
## http://stats.grok.se/json/en/201308/Procter_&%20Gamble
## http://stats.grok.se/json/en/201309/Procter_&%20Gamble
## http://stats.grok.se/json/en/201310/Procter_&%20Gamble
## http://stats.grok.se/json/en/201311/Procter_&%20Gamble
## http://stats.grok.se/json/en/201312/Procter_&%20Gamble
## http://stats.grok.se/json/en/201401/Procter_&%20Gamble
## http://stats.grok.se/json/en/201402/Procter_&%20Gamble
## http://stats.grok.se/json/en/201403/Procter_&%20Gamble
## http://stats.grok.se/json/en/201404/Procter_&%20Gamble
## http://stats.grok.se/json/en/201405/Procter_&%20Gamble
## http://stats.grok.se/json/en/201406/Procter_&%20Gamble
## http://stats.grok.se/json/en/201407/Procter_&%20Gamble
## http://stats.grok.se/json/en/201408/Procter_&%20Gamble
## http://stats.grok.se/json/en/201409/Procter_&%20Gamble
## http://stats.grok.se/json/en/201410/Procter_&%20Gamble
## http://stats.grok.se/json/en/201411/Procter_&%20Gamble
## http://stats.grok.se/json/en/201412/Procter_&%20Gamble
## http://stats.grok.se/json/en/201501/Procter_&%20Gamble
## http://stats.grok.se/json/en/201502/Procter_&%20Gamble
## http://stats.grok.se/json/en/201503/Procter_&%20Gamble
## http://stats.grok.se/json/en/201504/Procter_&%20Gamble
## http://stats.grok.se/json/en/201505/Procter_&%20Gamble
## http://stats.grok.se/json/en/201506/Procter_&%20Gamble
## http://stats.grok.se/json/en/201507/Procter_&%20Gamble
## http://stats.grok.se/json/en/201508/Procter_&%20Gamble
## http://stats.grok.se/json/en/201509/Procter_&%20Gamble
## http://stats.grok.se/json/en/201510/Procter_&%20Gamble
## http://stats.grok.se/json/en/201511/Procter_&%20Gamble
## http://stats.grok.se/json/en/201512/Procter_&%20Gamble
views7<-wp_trend(page ="Goldman Sachs" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/Goldman_Sachs
## http://stats.grok.se/json/en/201002/Goldman_Sachs
## http://stats.grok.se/json/en/201003/Goldman_Sachs
## http://stats.grok.se/json/en/201004/Goldman_Sachs
## http://stats.grok.se/json/en/201005/Goldman_Sachs
## http://stats.grok.se/json/en/201006/Goldman_Sachs
## http://stats.grok.se/json/en/201007/Goldman_Sachs
## http://stats.grok.se/json/en/201008/Goldman_Sachs
## http://stats.grok.se/json/en/201009/Goldman_Sachs
## http://stats.grok.se/json/en/201010/Goldman_Sachs
## http://stats.grok.se/json/en/201011/Goldman_Sachs
## http://stats.grok.se/json/en/201012/Goldman_Sachs
## http://stats.grok.se/json/en/201101/Goldman_Sachs
## http://stats.grok.se/json/en/201102/Goldman_Sachs
## http://stats.grok.se/json/en/201103/Goldman_Sachs
## http://stats.grok.se/json/en/201104/Goldman_Sachs
## http://stats.grok.se/json/en/201105/Goldman_Sachs
## http://stats.grok.se/json/en/201106/Goldman_Sachs
## http://stats.grok.se/json/en/201107/Goldman_Sachs
## http://stats.grok.se/json/en/201108/Goldman_Sachs
## http://stats.grok.se/json/en/201109/Goldman_Sachs
## http://stats.grok.se/json/en/201110/Goldman_Sachs
## http://stats.grok.se/json/en/201111/Goldman_Sachs
## http://stats.grok.se/json/en/201112/Goldman_Sachs
## http://stats.grok.se/json/en/201201/Goldman_Sachs
## http://stats.grok.se/json/en/201202/Goldman_Sachs
## http://stats.grok.se/json/en/201203/Goldman_Sachs
## http://stats.grok.se/json/en/201204/Goldman_Sachs
## http://stats.grok.se/json/en/201205/Goldman_Sachs
## http://stats.grok.se/json/en/201206/Goldman_Sachs
## http://stats.grok.se/json/en/201207/Goldman_Sachs
## http://stats.grok.se/json/en/201208/Goldman_Sachs
## http://stats.grok.se/json/en/201209/Goldman_Sachs
## http://stats.grok.se/json/en/201210/Goldman_Sachs
## http://stats.grok.se/json/en/201211/Goldman_Sachs
## http://stats.grok.se/json/en/201212/Goldman_Sachs
## http://stats.grok.se/json/en/201301/Goldman_Sachs
## http://stats.grok.se/json/en/201302/Goldman_Sachs
## http://stats.grok.se/json/en/201303/Goldman_Sachs
## http://stats.grok.se/json/en/201304/Goldman_Sachs
## http://stats.grok.se/json/en/201305/Goldman_Sachs
## http://stats.grok.se/json/en/201306/Goldman_Sachs
## http://stats.grok.se/json/en/201307/Goldman_Sachs
## http://stats.grok.se/json/en/201308/Goldman_Sachs
## http://stats.grok.se/json/en/201309/Goldman_Sachs
## http://stats.grok.se/json/en/201310/Goldman_Sachs
## http://stats.grok.se/json/en/201311/Goldman_Sachs
## http://stats.grok.se/json/en/201312/Goldman_Sachs
## http://stats.grok.se/json/en/201401/Goldman_Sachs
## http://stats.grok.se/json/en/201402/Goldman_Sachs
## http://stats.grok.se/json/en/201403/Goldman_Sachs
## http://stats.grok.se/json/en/201404/Goldman_Sachs
## http://stats.grok.se/json/en/201405/Goldman_Sachs
## http://stats.grok.se/json/en/201406/Goldman_Sachs
## http://stats.grok.se/json/en/201407/Goldman_Sachs
## http://stats.grok.se/json/en/201408/Goldman_Sachs
## http://stats.grok.se/json/en/201409/Goldman_Sachs
## http://stats.grok.se/json/en/201410/Goldman_Sachs
## http://stats.grok.se/json/en/201411/Goldman_Sachs
## http://stats.grok.se/json/en/201412/Goldman_Sachs
## http://stats.grok.se/json/en/201501/Goldman_Sachs
## http://stats.grok.se/json/en/201502/Goldman_Sachs
## http://stats.grok.se/json/en/201503/Goldman_Sachs
## http://stats.grok.se/json/en/201504/Goldman_Sachs
## http://stats.grok.se/json/en/201505/Goldman_Sachs
## http://stats.grok.se/json/en/201506/Goldman_Sachs
## http://stats.grok.se/json/en/201507/Goldman_Sachs
## http://stats.grok.se/json/en/201508/Goldman_Sachs
## http://stats.grok.se/json/en/201509/Goldman_Sachs
## http://stats.grok.se/json/en/201510/Goldman_Sachs
## http://stats.grok.se/json/en/201511/Goldman_Sachs
## http://stats.grok.se/json/en/201512/Goldman_Sachs
views8<-wp_trend(page ="Cisco Systems" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/Cisco_Systems
## http://stats.grok.se/json/en/201002/Cisco_Systems
## http://stats.grok.se/json/en/201003/Cisco_Systems
## http://stats.grok.se/json/en/201004/Cisco_Systems
## http://stats.grok.se/json/en/201005/Cisco_Systems
## http://stats.grok.se/json/en/201006/Cisco_Systems
## http://stats.grok.se/json/en/201007/Cisco_Systems
## http://stats.grok.se/json/en/201008/Cisco_Systems
## http://stats.grok.se/json/en/201009/Cisco_Systems
## http://stats.grok.se/json/en/201010/Cisco_Systems
## http://stats.grok.se/json/en/201011/Cisco_Systems
## http://stats.grok.se/json/en/201012/Cisco_Systems
## http://stats.grok.se/json/en/201101/Cisco_Systems
## http://stats.grok.se/json/en/201102/Cisco_Systems
## http://stats.grok.se/json/en/201103/Cisco_Systems
## http://stats.grok.se/json/en/201104/Cisco_Systems
## http://stats.grok.se/json/en/201105/Cisco_Systems
## http://stats.grok.se/json/en/201106/Cisco_Systems
## http://stats.grok.se/json/en/201107/Cisco_Systems
## http://stats.grok.se/json/en/201108/Cisco_Systems
## http://stats.grok.se/json/en/201109/Cisco_Systems
## http://stats.grok.se/json/en/201110/Cisco_Systems
## http://stats.grok.se/json/en/201111/Cisco_Systems
## http://stats.grok.se/json/en/201112/Cisco_Systems
## http://stats.grok.se/json/en/201201/Cisco_Systems
## http://stats.grok.se/json/en/201202/Cisco_Systems
## http://stats.grok.se/json/en/201203/Cisco_Systems
## http://stats.grok.se/json/en/201204/Cisco_Systems
## http://stats.grok.se/json/en/201205/Cisco_Systems
## http://stats.grok.se/json/en/201206/Cisco_Systems
## http://stats.grok.se/json/en/201207/Cisco_Systems
## http://stats.grok.se/json/en/201208/Cisco_Systems
## http://stats.grok.se/json/en/201209/Cisco_Systems
## http://stats.grok.se/json/en/201210/Cisco_Systems
## http://stats.grok.se/json/en/201211/Cisco_Systems
## http://stats.grok.se/json/en/201212/Cisco_Systems
## http://stats.grok.se/json/en/201301/Cisco_Systems
## http://stats.grok.se/json/en/201302/Cisco_Systems
## http://stats.grok.se/json/en/201303/Cisco_Systems
## http://stats.grok.se/json/en/201304/Cisco_Systems
## http://stats.grok.se/json/en/201305/Cisco_Systems
## http://stats.grok.se/json/en/201306/Cisco_Systems
## http://stats.grok.se/json/en/201307/Cisco_Systems
## http://stats.grok.se/json/en/201308/Cisco_Systems
## http://stats.grok.se/json/en/201309/Cisco_Systems
## http://stats.grok.se/json/en/201310/Cisco_Systems
## http://stats.grok.se/json/en/201311/Cisco_Systems
## http://stats.grok.se/json/en/201312/Cisco_Systems
## http://stats.grok.se/json/en/201401/Cisco_Systems
## http://stats.grok.se/json/en/201402/Cisco_Systems
## http://stats.grok.se/json/en/201403/Cisco_Systems
## http://stats.grok.se/json/en/201404/Cisco_Systems
## http://stats.grok.se/json/en/201405/Cisco_Systems
## http://stats.grok.se/json/en/201406/Cisco_Systems
## http://stats.grok.se/json/en/201407/Cisco_Systems
## http://stats.grok.se/json/en/201408/Cisco_Systems
## http://stats.grok.se/json/en/201409/Cisco_Systems
## http://stats.grok.se/json/en/201410/Cisco_Systems
## http://stats.grok.se/json/en/201411/Cisco_Systems
## http://stats.grok.se/json/en/201412/Cisco_Systems
## http://stats.grok.se/json/en/201501/Cisco_Systems
## http://stats.grok.se/json/en/201502/Cisco_Systems
## http://stats.grok.se/json/en/201503/Cisco_Systems
## http://stats.grok.se/json/en/201504/Cisco_Systems
## http://stats.grok.se/json/en/201505/Cisco_Systems
## http://stats.grok.se/json/en/201506/Cisco_Systems
## http://stats.grok.se/json/en/201507/Cisco_Systems
## http://stats.grok.se/json/en/201508/Cisco_Systems
## http://stats.grok.se/json/en/201509/Cisco_Systems
## http://stats.grok.se/json/en/201510/Cisco_Systems
## http://stats.grok.se/json/en/201511/Cisco_Systems
## http://stats.grok.se/json/en/201512/Cisco_Systems
views9<-wp_trend(page ="Pfizer" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/Pfizer
## http://stats.grok.se/json/en/201002/Pfizer
## http://stats.grok.se/json/en/201003/Pfizer
## http://stats.grok.se/json/en/201004/Pfizer
## http://stats.grok.se/json/en/201005/Pfizer
## http://stats.grok.se/json/en/201006/Pfizer
## http://stats.grok.se/json/en/201007/Pfizer
## http://stats.grok.se/json/en/201008/Pfizer
## http://stats.grok.se/json/en/201009/Pfizer
## http://stats.grok.se/json/en/201010/Pfizer
## http://stats.grok.se/json/en/201011/Pfizer
## http://stats.grok.se/json/en/201012/Pfizer
## http://stats.grok.se/json/en/201101/Pfizer
## http://stats.grok.se/json/en/201102/Pfizer
## http://stats.grok.se/json/en/201103/Pfizer
## http://stats.grok.se/json/en/201104/Pfizer
## http://stats.grok.se/json/en/201105/Pfizer
## http://stats.grok.se/json/en/201106/Pfizer
## http://stats.grok.se/json/en/201107/Pfizer
## http://stats.grok.se/json/en/201108/Pfizer
## http://stats.grok.se/json/en/201109/Pfizer
## http://stats.grok.se/json/en/201110/Pfizer
## http://stats.grok.se/json/en/201111/Pfizer
## http://stats.grok.se/json/en/201112/Pfizer
## http://stats.grok.se/json/en/201201/Pfizer
## http://stats.grok.se/json/en/201202/Pfizer
## http://stats.grok.se/json/en/201203/Pfizer
## http://stats.grok.se/json/en/201204/Pfizer
## http://stats.grok.se/json/en/201205/Pfizer
## http://stats.grok.se/json/en/201206/Pfizer
## http://stats.grok.se/json/en/201207/Pfizer
## http://stats.grok.se/json/en/201208/Pfizer
## http://stats.grok.se/json/en/201209/Pfizer
## http://stats.grok.se/json/en/201210/Pfizer
## http://stats.grok.se/json/en/201211/Pfizer
## http://stats.grok.se/json/en/201212/Pfizer
## http://stats.grok.se/json/en/201301/Pfizer
## http://stats.grok.se/json/en/201302/Pfizer
## http://stats.grok.se/json/en/201303/Pfizer
## http://stats.grok.se/json/en/201304/Pfizer
## http://stats.grok.se/json/en/201305/Pfizer
## http://stats.grok.se/json/en/201306/Pfizer
## http://stats.grok.se/json/en/201307/Pfizer
## http://stats.grok.se/json/en/201308/Pfizer
## http://stats.grok.se/json/en/201309/Pfizer
## http://stats.grok.se/json/en/201310/Pfizer
## http://stats.grok.se/json/en/201311/Pfizer
## http://stats.grok.se/json/en/201312/Pfizer
## http://stats.grok.se/json/en/201401/Pfizer
## http://stats.grok.se/json/en/201402/Pfizer
## http://stats.grok.se/json/en/201403/Pfizer
## http://stats.grok.se/json/en/201404/Pfizer
## http://stats.grok.se/json/en/201405/Pfizer
## http://stats.grok.se/json/en/201406/Pfizer
## http://stats.grok.se/json/en/201407/Pfizer
## http://stats.grok.se/json/en/201408/Pfizer
## http://stats.grok.se/json/en/201409/Pfizer
## http://stats.grok.se/json/en/201410/Pfizer
## http://stats.grok.se/json/en/201411/Pfizer
## http://stats.grok.se/json/en/201412/Pfizer
## http://stats.grok.se/json/en/201501/Pfizer
## http://stats.grok.se/json/en/201502/Pfizer
## http://stats.grok.se/json/en/201503/Pfizer
## http://stats.grok.se/json/en/201504/Pfizer
## http://stats.grok.se/json/en/201505/Pfizer
## http://stats.grok.se/json/en/201506/Pfizer
## http://stats.grok.se/json/en/201507/Pfizer
## http://stats.grok.se/json/en/201508/Pfizer
## http://stats.grok.se/json/en/201509/Pfizer
## http://stats.grok.se/json/en/201510/Pfizer
## http://stats.grok.se/json/en/201511/Pfizer
## http://stats.grok.se/json/en/201512/Pfizer
views10<-wp_trend(page ="UnitedHealth Group" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/UnitedHealth_Group
## http://stats.grok.se/json/en/201002/UnitedHealth_Group
## http://stats.grok.se/json/en/201003/UnitedHealth_Group
## http://stats.grok.se/json/en/201004/UnitedHealth_Group
## http://stats.grok.se/json/en/201005/UnitedHealth_Group
## http://stats.grok.se/json/en/201006/UnitedHealth_Group
## http://stats.grok.se/json/en/201007/UnitedHealth_Group
## http://stats.grok.se/json/en/201008/UnitedHealth_Group
## http://stats.grok.se/json/en/201009/UnitedHealth_Group
## http://stats.grok.se/json/en/201010/UnitedHealth_Group
## http://stats.grok.se/json/en/201011/UnitedHealth_Group
## http://stats.grok.se/json/en/201012/UnitedHealth_Group
## http://stats.grok.se/json/en/201101/UnitedHealth_Group
## http://stats.grok.se/json/en/201102/UnitedHealth_Group
## http://stats.grok.se/json/en/201103/UnitedHealth_Group
## http://stats.grok.se/json/en/201104/UnitedHealth_Group
## http://stats.grok.se/json/en/201105/UnitedHealth_Group
## http://stats.grok.se/json/en/201106/UnitedHealth_Group
## http://stats.grok.se/json/en/201107/UnitedHealth_Group
## http://stats.grok.se/json/en/201108/UnitedHealth_Group
## http://stats.grok.se/json/en/201109/UnitedHealth_Group
## http://stats.grok.se/json/en/201110/UnitedHealth_Group
## http://stats.grok.se/json/en/201111/UnitedHealth_Group
## http://stats.grok.se/json/en/201112/UnitedHealth_Group
## http://stats.grok.se/json/en/201201/UnitedHealth_Group
## http://stats.grok.se/json/en/201202/UnitedHealth_Group
## http://stats.grok.se/json/en/201203/UnitedHealth_Group
## http://stats.grok.se/json/en/201204/UnitedHealth_Group
## http://stats.grok.se/json/en/201205/UnitedHealth_Group
## http://stats.grok.se/json/en/201206/UnitedHealth_Group
## http://stats.grok.se/json/en/201207/UnitedHealth_Group
## http://stats.grok.se/json/en/201208/UnitedHealth_Group
## http://stats.grok.se/json/en/201209/UnitedHealth_Group
## http://stats.grok.se/json/en/201210/UnitedHealth_Group
## http://stats.grok.se/json/en/201211/UnitedHealth_Group
## http://stats.grok.se/json/en/201212/UnitedHealth_Group
## http://stats.grok.se/json/en/201301/UnitedHealth_Group
## http://stats.grok.se/json/en/201302/UnitedHealth_Group
## http://stats.grok.se/json/en/201303/UnitedHealth_Group
## http://stats.grok.se/json/en/201304/UnitedHealth_Group
## http://stats.grok.se/json/en/201305/UnitedHealth_Group
## http://stats.grok.se/json/en/201306/UnitedHealth_Group
## http://stats.grok.se/json/en/201307/UnitedHealth_Group
## http://stats.grok.se/json/en/201308/UnitedHealth_Group
## http://stats.grok.se/json/en/201309/UnitedHealth_Group
## http://stats.grok.se/json/en/201310/UnitedHealth_Group
## http://stats.grok.se/json/en/201311/UnitedHealth_Group
## http://stats.grok.se/json/en/201312/UnitedHealth_Group
## http://stats.grok.se/json/en/201401/UnitedHealth_Group
## http://stats.grok.se/json/en/201402/UnitedHealth_Group
## http://stats.grok.se/json/en/201403/UnitedHealth_Group
## http://stats.grok.se/json/en/201404/UnitedHealth_Group
## http://stats.grok.se/json/en/201405/UnitedHealth_Group
## http://stats.grok.se/json/en/201406/UnitedHealth_Group
## http://stats.grok.se/json/en/201407/UnitedHealth_Group
## http://stats.grok.se/json/en/201408/UnitedHealth_Group
## http://stats.grok.se/json/en/201409/UnitedHealth_Group
## http://stats.grok.se/json/en/201410/UnitedHealth_Group
## http://stats.grok.se/json/en/201411/UnitedHealth_Group
## http://stats.grok.se/json/en/201412/UnitedHealth_Group
## http://stats.grok.se/json/en/201501/UnitedHealth_Group
## http://stats.grok.se/json/en/201502/UnitedHealth_Group
## http://stats.grok.se/json/en/201503/UnitedHealth_Group
## http://stats.grok.se/json/en/201504/UnitedHealth_Group
## http://stats.grok.se/json/en/201505/UnitedHealth_Group
## http://stats.grok.se/json/en/201506/UnitedHealth_Group
## http://stats.grok.se/json/en/201507/UnitedHealth_Group
## http://stats.grok.se/json/en/201508/UnitedHealth_Group
## http://stats.grok.se/json/en/201509/UnitedHealth_Group
## http://stats.grok.se/json/en/201510/UnitedHealth_Group
## http://stats.grok.se/json/en/201511/UnitedHealth_Group
## http://stats.grok.se/json/en/201512/UnitedHealth_Group
views11<-wp_trend(page ="IBM" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/IBM
## http://stats.grok.se/json/en/201002/IBM
## http://stats.grok.se/json/en/201003/IBM
## http://stats.grok.se/json/en/201004/IBM
## http://stats.grok.se/json/en/201005/IBM
## http://stats.grok.se/json/en/201006/IBM
## http://stats.grok.se/json/en/201007/IBM
## http://stats.grok.se/json/en/201008/IBM
## http://stats.grok.se/json/en/201009/IBM
## http://stats.grok.se/json/en/201010/IBM
## http://stats.grok.se/json/en/201011/IBM
## http://stats.grok.se/json/en/201012/IBM
## http://stats.grok.se/json/en/201101/IBM
## http://stats.grok.se/json/en/201102/IBM
## http://stats.grok.se/json/en/201103/IBM
## http://stats.grok.se/json/en/201104/IBM
## http://stats.grok.se/json/en/201105/IBM
## http://stats.grok.se/json/en/201106/IBM
## http://stats.grok.se/json/en/201107/IBM
## http://stats.grok.se/json/en/201108/IBM
## http://stats.grok.se/json/en/201109/IBM
## http://stats.grok.se/json/en/201110/IBM
## http://stats.grok.se/json/en/201111/IBM
## http://stats.grok.se/json/en/201112/IBM
## http://stats.grok.se/json/en/201201/IBM
## http://stats.grok.se/json/en/201202/IBM
## http://stats.grok.se/json/en/201203/IBM
## http://stats.grok.se/json/en/201204/IBM
## http://stats.grok.se/json/en/201205/IBM
## http://stats.grok.se/json/en/201206/IBM
## http://stats.grok.se/json/en/201207/IBM
## http://stats.grok.se/json/en/201208/IBM
## http://stats.grok.se/json/en/201209/IBM
## http://stats.grok.se/json/en/201210/IBM
## http://stats.grok.se/json/en/201211/IBM
## http://stats.grok.se/json/en/201212/IBM
## http://stats.grok.se/json/en/201301/IBM
## http://stats.grok.se/json/en/201302/IBM
## http://stats.grok.se/json/en/201303/IBM
## http://stats.grok.se/json/en/201304/IBM
## http://stats.grok.se/json/en/201305/IBM
## http://stats.grok.se/json/en/201306/IBM
## http://stats.grok.se/json/en/201307/IBM
## http://stats.grok.se/json/en/201308/IBM
## http://stats.grok.se/json/en/201309/IBM
## http://stats.grok.se/json/en/201310/IBM
## http://stats.grok.se/json/en/201311/IBM
## http://stats.grok.se/json/en/201312/IBM
## http://stats.grok.se/json/en/201401/IBM
## http://stats.grok.se/json/en/201402/IBM
## http://stats.grok.se/json/en/201403/IBM
## http://stats.grok.se/json/en/201404/IBM
## http://stats.grok.se/json/en/201405/IBM
## http://stats.grok.se/json/en/201406/IBM
## http://stats.grok.se/json/en/201407/IBM
## http://stats.grok.se/json/en/201408/IBM
## http://stats.grok.se/json/en/201409/IBM
## http://stats.grok.se/json/en/201410/IBM
## http://stats.grok.se/json/en/201411/IBM
## http://stats.grok.se/json/en/201412/IBM
## http://stats.grok.se/json/en/201501/IBM
## http://stats.grok.se/json/en/201502/IBM
## http://stats.grok.se/json/en/201503/IBM
## http://stats.grok.se/json/en/201504/IBM
## http://stats.grok.se/json/en/201505/IBM
## http://stats.grok.se/json/en/201506/IBM
## http://stats.grok.se/json/en/201507/IBM
## http://stats.grok.se/json/en/201508/IBM
## http://stats.grok.se/json/en/201509/IBM
## http://stats.grok.se/json/en/201510/IBM
## http://stats.grok.se/json/en/201511/IBM
## http://stats.grok.se/json/en/201512/IBM
views5<-wp_trend(page ="McDonald's" ,from =startDate ,to =endDate ,lang = "en",friendly = TRUE,requestFrom = "wp.trend.tester at wptt.wptt",userAgent = TRUE)
## Option 'requestFrom' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## Option 'friendly' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## The package now is friendly by default.
##
## Option 'userAgent' is deprecated and will cause errors
## in futuere versions of the wp_trend() function. Please read
## the package vignette and/or README to learn about the new
## set of options.
##
## Check wp_http_header() to know which information are send to
## stats.grok.se (R and package versions)
##
## http://stats.grok.se/json/en/201001/McDonald's
## http://stats.grok.se/json/en/201002/McDonald's
## http://stats.grok.se/json/en/201003/McDonald's
## http://stats.grok.se/json/en/201004/McDonald's
## http://stats.grok.se/json/en/201005/McDonald's
## http://stats.grok.se/json/en/201006/McDonald's
## http://stats.grok.se/json/en/201007/McDonald's
## http://stats.grok.se/json/en/201008/McDonald's
## http://stats.grok.se/json/en/201009/McDonald's
## http://stats.grok.se/json/en/201010/McDonald's
## http://stats.grok.se/json/en/201011/McDonald's
## http://stats.grok.se/json/en/201012/McDonald's
## http://stats.grok.se/json/en/201101/McDonald's
## http://stats.grok.se/json/en/201102/McDonald's
## http://stats.grok.se/json/en/201103/McDonald's
## http://stats.grok.se/json/en/201104/McDonald's
## http://stats.grok.se/json/en/201105/McDonald's
## http://stats.grok.se/json/en/201106/McDonald's
## http://stats.grok.se/json/en/201107/McDonald's
## http://stats.grok.se/json/en/201108/McDonald's
## http://stats.grok.se/json/en/201109/McDonald's
## http://stats.grok.se/json/en/201110/McDonald's
## http://stats.grok.se/json/en/201111/McDonald's
## http://stats.grok.se/json/en/201112/McDonald's
## http://stats.grok.se/json/en/201201/McDonald's
## http://stats.grok.se/json/en/201202/McDonald's
## http://stats.grok.se/json/en/201203/McDonald's
## http://stats.grok.se/json/en/201204/McDonald's
## http://stats.grok.se/json/en/201205/McDonald's
## http://stats.grok.se/json/en/201206/McDonald's
## http://stats.grok.se/json/en/201207/McDonald's
## http://stats.grok.se/json/en/201208/McDonald's
## http://stats.grok.se/json/en/201209/McDonald's
## http://stats.grok.se/json/en/201210/McDonald's
## http://stats.grok.se/json/en/201211/McDonald's
## http://stats.grok.se/json/en/201212/McDonald's
## http://stats.grok.se/json/en/201301/McDonald's
## http://stats.grok.se/json/en/201302/McDonald's
## http://stats.grok.se/json/en/201303/McDonald's
## http://stats.grok.se/json/en/201304/McDonald's
## http://stats.grok.se/json/en/201305/McDonald's
## http://stats.grok.se/json/en/201306/McDonald's
## http://stats.grok.se/json/en/201307/McDonald's
## http://stats.grok.se/json/en/201308/McDonald's
## http://stats.grok.se/json/en/201309/McDonald's
## http://stats.grok.se/json/en/201310/McDonald's
## http://stats.grok.se/json/en/201311/McDonald's
## http://stats.grok.se/json/en/201312/McDonald's
## http://stats.grok.se/json/en/201401/McDonald's
## http://stats.grok.se/json/en/201402/McDonald's
## http://stats.grok.se/json/en/201403/McDonald's
## http://stats.grok.se/json/en/201404/McDonald's
## http://stats.grok.se/json/en/201405/McDonald's
## http://stats.grok.se/json/en/201406/McDonald's
## http://stats.grok.se/json/en/201407/McDonald's
## http://stats.grok.se/json/en/201408/McDonald's
## http://stats.grok.se/json/en/201409/McDonald's
## http://stats.grok.se/json/en/201410/McDonald's
## http://stats.grok.se/json/en/201411/McDonald's
## http://stats.grok.se/json/en/201412/McDonald's
## http://stats.grok.se/json/en/201501/McDonald's
## http://stats.grok.se/json/en/201502/McDonald's
## http://stats.grok.se/json/en/201503/McDonald's
## http://stats.grok.se/json/en/201504/McDonald's
## http://stats.grok.se/json/en/201505/McDonald's
## http://stats.grok.se/json/en/201506/McDonald's
## http://stats.grok.se/json/en/201507/McDonald's
## http://stats.grok.se/json/en/201508/McDonald's
## http://stats.grok.se/json/en/201509/McDonald's
## http://stats.grok.se/json/en/201510/McDonald's
## http://stats.grok.se/json/en/201511/McDonald's
## http://stats.grok.se/json/en/201512/McDonald's
viewdf1<-cbind(views1[,1:2],views2[,2],views3[,2],views4[,2],views5[,2],views6[,2],views7[,2],views8[,2],views9[,2],views10[,2],views11[,2])
CombDF1<-merge(viewdf1,DJIADF, by.x='date', by.y='date')
DataSet<-data.frame(RSI3,EMAcross,MACDsignal,SMI,BBp,CCI20,DEMA10c)
DataSet<-DataSet[-c(1:33),]
Alldata<-cbind(DataSet,CombDF1[33:1509,2:12])
Normalized <-function(x) {(x-min(x))/(max(x)-min(x))}
NormalizedData<-as.data.frame(lapply(Alldata,Normalized))
ClassDF<-data.frame(date = index(Class), Class, row.names=NULL)
AlldataNormalized<-data.frame(NormalizedData,ClassDF[33:1509,2])
colnames(AlldataNormalized)<-c("RSI3","EMAcross","MACDsignal","SMI","BBp","CCI20","DEMA10c","Views1","Views2","Views3","Views4","Views5","Views6","Views7","Views8","Views9","Views10","Views11","Class")
TrainingSet<-AlldataNormalized[1:1000,]
TestSet<-AlldataNormalized[1001:1477,]
TrainClass<-TrainingSet[,19]
TrainPred<-TrainingSet[,-19]
TestClass<-TestSet[,19]
TestPred<-TestSet[,-19]
library(h2o)
## Loading required package: statmod
##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit http://docs.h2o.ai
##
## ----------------------------------------------------------------------
##
##
## Attaching package: 'h2o'
##
## The following objects are masked from 'package:stats':
##
## sd, var
##
## The following objects are masked from 'package:base':
##
## %*%, apply, as.factor, as.numeric, colnames, colnames<-,
## ifelse, %in%, is.factor, is.numeric, log, trunc
localH2O <- h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## /tmp/RtmptyRMPQ/h2o_mitra2_started_from_r.out
## /tmp/RtmptyRMPQ/h2o_mitra2_started_from_r.err
##
##
## ...Successfully connected to http://localhost:54321/
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 2 seconds 450 milliseconds
## H2O cluster version: 3.6.0.8
## H2O cluster name: H2O_started_from_R_mitra2_nwe867
## H2O cluster total nodes: 1
## H2O cluster total memory: 0.66 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 2
## H2O cluster healthy: TRUE
##
## Note: As started, H2O is limited to the CRAN default of 2 CPUs.
## Shut down and restart H2O as shown below to use all your CPUs.
## > h2o.shutdown()
## > h2o.init(nthreads = -1)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE,
Xmx = '2g')
## Warning in h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, Xmx =
## "2g"): Xmx is a deprecated parameter. Use `max_mem_size` and `min_mem_size`
## to set the memory boundaries. Using `Xmx` to set these.
## Successfully connected to http://localhost:54321/
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 2 seconds 598 milliseconds
## H2O cluster version: 3.6.0.8
## H2O cluster name: H2O_started_from_R_mitra2_nwe867
## H2O cluster total nodes: 1
## H2O cluster total memory: 0.66 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 2
## H2O cluster healthy: TRUE
TrainH2o<-as.h2o(TrainingSet, destination_frame = "TrainH2o")
##
|
| | 0%
|
|=================================================================| 100%
TestH2o<-as.h2o(TestPred, destination_frame = "TestH2o")
##
|
| | 0%
|
|=================================================================| 100%
model <- h2o.deeplearning(x = 1:18,y = 19,training_frame = TrainH2o, activation = "MaxoutWithDropout",hidden = c(200,200),epochs = 200,rate_decay =5e-4, l1=1e-5)
##
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|== | 4%
|
|=== | 4%
|
|=== | 5%
|
|==== | 6%
|
|==== | 7%
|
|===== | 7%
|
|===== | 8%
|
|====== | 9%
|
|====== | 10%
|
|======= | 10%
|
|======= | 11%
|
|======== | 12%
|
|======== | 13%
|
|========= | 14%
|
|========== | 15%
|
|========== | 16%
|
|=========== | 17%
|
|============ | 18%
|
|============ | 19%
|
|============= | 20%
|
|============== | 21%
|
|============== | 22%
|
|=============== | 23%
|
|================ | 24%
|
|================ | 25%
|
|================= | 26%
|
|================== | 27%
|
|================== | 28%
|
|=================== | 29%
|
|=================== | 30%
|
|==================== | 30%
|
|==================== | 31%
|
|===================== | 32%
|
|===================== | 33%
|
|====================== | 33%
|
|====================== | 34%
|
|======================= | 35%
|
|======================= | 36%
|
|======================== | 36%
|
|======================== | 37%
|
|======================== | 38%
|
|========================= | 38%
|
|========================= | 39%
|
|========================== | 39%
|
|========================== | 40%
|
|========================== | 41%
|
|=========================== | 41%
|
|=========================== | 42%
|
|============================ | 42%
|
|============================ | 43%
|
|============================ | 44%
|
|============================= | 44%
|
|============================= | 45%
|
|============================== | 46%
|
|============================== | 47%
|
|=============================== | 47%
|
|=============================== | 48%
|
|================================ | 49%
|
|================================ | 50%
|
|================================= | 50%
|
|================================= | 51%
|
|================================== | 52%
|
|================================== | 53%
|
|=================================================================| 100%
h2o_yhat_test <- h2o.predict(model,TestH2o)
df_yhat_test <- as.data.frame(h2o_yhat_test)
prediction <-df_yhat_test[,1]
confusionMatrix(prediction,TestClass)
## Confusion Matrix and Statistics
##
## Reference
## Prediction DOWN UP
## DOWN 169 55
## UP 57 196
##
## Accuracy : 0.7652
## 95% CI : (0.7245, 0.8025)
## No Information Rate : 0.5262
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.5289
## Mcnemar's Test P-Value : 0.9247
##
## Sensitivity : 0.7478
## Specificity : 0.7809
## Pos Pred Value : 0.7545
## Neg Pred Value : 0.7747
## Prevalence : 0.4738
## Detection Rate : 0.3543
## Detection Prevalence : 0.4696
## Balanced Accuracy : 0.7643
##
## 'Positive' Class : DOWN
##
hyper_params <- list(
hidden=list(c(200,200), c(100,300,100), c(500,500,500),c(100,100,150)),
input_dropout_ratio=c(0,0.05),
rate=c(0.01,0.02),
rate_annealing=c(1e-8,1e-7,1e-6)
)
grid <- h2o.grid( "deeplearning", model_id="dl_grid", training_frame=TrainH2o, x=1:18,y=19,
epochs=100,
stopping_metric="misclassification",
stopping_tolerance=1e-2, ## stop when logloss does not improve by >=1% for 2 scoring events
stopping_rounds=2,
score_validation_samples=10000, ## downsample validation set for faster scoring
score_duty_cycle=0.025, ## don't score more than 2.5% of the wall time
adaptive_rate=F, ## manually tuned learning rate
momentum_start=0.5, ## manually tuned momentum
momentum_stable=0.9,
momentum_ramp=1e7,
l1=1e-5,
l2=1e-5,
activation=c("TanhWithDropout"),
max_w2=10, ## can help improve stability for Rectifier
hyper_params=hyper_params
)
##
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|===== | 8%
|
|======= | 10%
|
|======== | 12%
|
|========= | 15%
|
|=========== | 17%
|
|============ | 19%
|
|============== | 21%
|
|=============== | 23%
|
|================ | 25%
|
|================== | 27%
|
|=================== | 29%
|
|==================== | 31%
|
|====================== | 33%
|
|======================= | 35%
|
|======================== | 38%
|
|========================== | 40%
|
|=========================== | 42%
|
|============================ | 44%
|
|============================== | 46%
|
|=============================== | 48%
|
|================================ | 50%
|
|================================== | 52%
|
|=================================== | 54%
|
|===================================== | 56%
|
|====================================== | 58%
|
|======================================= | 60%
|
|========================================= | 62%
|
|========================================== | 65%
|
|=========================================== | 67%
|
|============================================= | 69%
|
|============================================== | 71%
|
|=============================================== | 73%
|
|================================================= | 75%
|
|================================================== | 77%
|
|=================================================== | 79%
|
|===================================================== | 81%
|
|====================================================== | 83%
|
|======================================================== | 85%
|
|========================================================= | 88%
|
|========================================================== | 90%
|
|============================================================ | 92%
|
|============================================================= | 94%
|
|============================================================== | 96%
|
|================================================================ | 98%
|
|=================================================================| 100%
model_grid <- h2o.grid("deeplearning",hyper_params = hyper_params,x = 1:18,y = 19,training_frame = TrainH2o,distribution = "multinomial", activation = "TanhWithDropout")
##
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|===== | 8%
|
|======= | 10%
|
|======== | 12%
|
|========= | 15%
|
|=========== | 17%
|
|============ | 19%
|
|============== | 21%
|
|=============== | 23%
|
|================ | 25%
|
|================== | 27%
|
|=================== | 29%
|
|==================== | 31%
|
|====================== | 33%
|
|======================= | 35%
|
|======================== | 38%
|
|========================== | 40%
|
|=========================== | 42%
|
|============================ | 44%
|
|============================== | 46%
|
|=============================== | 48%
|
|================================ | 50%
|
|================================== | 52%
|
|=================================== | 54%
|
|===================================== | 56%
|
|====================================== | 58%
|
|======================================= | 60%
|
|========================================= | 62%
|
|========================================== | 65%
|
|=========================================== | 67%
|
|============================================= | 69%
|
|============================================== | 71%
|
|=============================================== | 73%
|
|================================================= | 75%
|
|================================================== | 77%
|
|=================================================== | 79%
|
|===================================================== | 81%
|
|====================================================== | 83%
|
|======================================================== | 85%
|
|========================================================= | 88%
|
|========================================================== | 90%
|
|============================================================ | 92%
|
|============================================================= | 94%
|
|============================================================== | 96%
|
|================================================================ | 98%
|
|=================================================================| 100%
summary(grid)
## H2O Grid Details
## ================
##
## Grid ID: Grid_DeepLearning_TrainH2o_model_R_1457905851217_7
## Used hyper parameters:
## - input_dropout_ratio
## - rate
## - hidden
## - rate_annealing
## Number of models: 48
## Number of failed models: 0
##
## Generated models
## ----------------
## input_dropout_ratio rate hidden rate_annealing status_ok
## 0.05 0.02 [200,200] 1e-07 OK
## 0.00 0.01 [500,500,500] 1e-07 OK
## 0.05 0.02 [500,500,500] 1e-07 OK
## 0.00 0.01 [500,500,500] 1e-08 OK
## 0.05 0.02 [100,100,150] 1e-08 OK
## 0.05 0.02 [500,500,500] 1e-06 OK
## 0.05 0.01 [200,200] 1e-06 OK
## 0.00 0.02 [100,300,100] 1e-06 OK
## 0.00 0.01 [100,100,150] 1e-08 OK
## 0.00 0.02 [100,300,100] 1e-07 OK
## 0.00 0.02 [200,200] 1e-07 OK
## 0.05 0.02 [500,500,500] 1e-08 OK
## 0.00 0.02 [100,100,150] 1e-06 OK
## 0.00 0.01 [100,300,100] 1e-08 OK
## 0.00 0.01 [200,200] 1e-07 OK
## 0.05 0.01 [500,500,500] 1e-07 OK
## 0.05 0.02 [100,300,100] 1e-07 OK
## 0.05 0.01 [200,200] 1e-08 OK
## 0.05 0.01 [500,500,500] 1e-08 OK
## 0.00 0.01 [100,300,100] 1e-06 OK
## 0.05 0.02 [100,300,100] 1e-06 OK
## 0.00 0.01 [100,100,150] 1e-06 OK
## 0.05 0.01 [100,100,150] 1e-06 OK
## 0.05 0.02 [100,100,150] 1e-07 OK
## 0.00 0.01 [200,200] 1e-06 OK
## 0.00 0.02 [500,500,500] 1e-06 OK
## 0.05 0.01 [500,500,500] 1e-06 OK
## 0.00 0.02 [500,500,500] 1e-08 OK
## 0.00 0.02 [200,200] 1e-06 OK
## 0.05 0.02 [100,100,150] 1e-06 OK
## 0.00 0.01 [100,100,150] 1e-07 OK
## 0.00 0.02 [200,200] 1e-08 OK
## 0.05 0.02 [100,300,100] 1e-08 OK
## 0.05 0.01 [100,100,150] 1e-07 OK
## 0.05 0.01 [100,300,100] 1e-07 OK
## 0.05 0.02 [200,200] 1e-08 OK
## 0.00 0.01 [100,300,100] 1e-07 OK
## 0.00 0.02 [100,300,100] 1e-08 OK
## 0.00 0.01 [500,500,500] 1e-06 OK
## 0.05 0.01 [100,300,100] 1e-08 OK
## 0.00 0.02 [100,100,150] 1e-07 OK
## 0.00 0.01 [200,200] 1e-08 OK
## 0.05 0.01 [200,200] 1e-07 OK
## 0.00 0.02 [100,100,150] 1e-08 OK
## 0.00 0.02 [500,500,500] 1e-07 OK
## 0.05 0.01 [100,300,100] 1e-06 OK
## 0.05 0.01 [100,100,150] 1e-08 OK
## 0.05 0.02 [200,200] 1e-06 OK
## model_ids
## dl_grid_model_1457905851217_8_19
## dl_grid_model_1457905851217_8_24
## dl_grid_model_1457905851217_8_27
## dl_grid_model_1457905851217_8_8
## dl_grid_model_1457905851217_8_15
## dl_grid_model_1457905851217_8_43
## dl_grid_model_1457905851217_8_33
## dl_grid_model_1457905851217_8_38
## dl_grid_model_1457905851217_8_12
## dl_grid_model_1457905851217_8_22
## dl_grid_model_1457905851217_8_18
## dl_grid_model_1457905851217_8_11
## dl_grid_model_1457905851217_8_46
## dl_grid_model_1457905851217_8_4
## dl_grid_model_1457905851217_8_16
## dl_grid_model_1457905851217_8_25
## dl_grid_model_1457905851217_8_23
## dl_grid_model_1457905851217_8_1
## dl_grid_model_1457905851217_8_9
## dl_grid_model_1457905851217_8_36
## dl_grid_model_1457905851217_8_39
## dl_grid_model_1457905851217_8_44
## dl_grid_model_1457905851217_8_45
## dl_grid_model_1457905851217_8_31
## dl_grid_model_1457905851217_8_32
## dl_grid_model_1457905851217_8_42
## dl_grid_model_1457905851217_8_41
## dl_grid_model_1457905851217_8_10
## dl_grid_model_1457905851217_8_34
## dl_grid_model_1457905851217_8_47
## dl_grid_model_1457905851217_8_28
## dl_grid_model_1457905851217_8_2
## dl_grid_model_1457905851217_8_7
## dl_grid_model_1457905851217_8_29
## dl_grid_model_1457905851217_8_21
## dl_grid_model_1457905851217_8_3
## dl_grid_model_1457905851217_8_20
## dl_grid_model_1457905851217_8_6
## dl_grid_model_1457905851217_8_40
## dl_grid_model_1457905851217_8_5
## dl_grid_model_1457905851217_8_30
## dl_grid_model_1457905851217_8_0
## dl_grid_model_1457905851217_8_17
## dl_grid_model_1457905851217_8_14
## dl_grid_model_1457905851217_8_26
## dl_grid_model_1457905851217_8_37
## dl_grid_model_1457905851217_8_13
## dl_grid_model_1457905851217_8_35
## H2O Grid Summary
## ================
##
## Grid ID: Grid_DeepLearning_TrainH2o_model_R_1457905851217_7
## Used hyper parameters:
## - input_dropout_ratio
## - rate
## - hidden
## - rate_annealing
## Number of models: 48
## - dl_grid_model_1457905851217_8_19
## - dl_grid_model_1457905851217_8_24
## - dl_grid_model_1457905851217_8_27
## - dl_grid_model_1457905851217_8_8
## - dl_grid_model_1457905851217_8_15
## - dl_grid_model_1457905851217_8_43
## - dl_grid_model_1457905851217_8_33
## - dl_grid_model_1457905851217_8_38
## - dl_grid_model_1457905851217_8_12
## - dl_grid_model_1457905851217_8_22
## - dl_grid_model_1457905851217_8_18
## - dl_grid_model_1457905851217_8_11
## - dl_grid_model_1457905851217_8_46
## - dl_grid_model_1457905851217_8_4
## - dl_grid_model_1457905851217_8_16
## - dl_grid_model_1457905851217_8_25
## - dl_grid_model_1457905851217_8_23
## - dl_grid_model_1457905851217_8_1
## - dl_grid_model_1457905851217_8_9
## - dl_grid_model_1457905851217_8_36
## - dl_grid_model_1457905851217_8_39
## - dl_grid_model_1457905851217_8_44
## - dl_grid_model_1457905851217_8_45
## - dl_grid_model_1457905851217_8_31
## - dl_grid_model_1457905851217_8_32
## - dl_grid_model_1457905851217_8_42
## - dl_grid_model_1457905851217_8_41
## - dl_grid_model_1457905851217_8_10
## - dl_grid_model_1457905851217_8_34
## - dl_grid_model_1457905851217_8_47
## - dl_grid_model_1457905851217_8_28
## - dl_grid_model_1457905851217_8_2
## - dl_grid_model_1457905851217_8_7
## - dl_grid_model_1457905851217_8_29
## - dl_grid_model_1457905851217_8_21
## - dl_grid_model_1457905851217_8_3
## - dl_grid_model_1457905851217_8_20
## - dl_grid_model_1457905851217_8_6
## - dl_grid_model_1457905851217_8_40
## - dl_grid_model_1457905851217_8_5
## - dl_grid_model_1457905851217_8_30
## - dl_grid_model_1457905851217_8_0
## - dl_grid_model_1457905851217_8_17
## - dl_grid_model_1457905851217_8_14
## - dl_grid_model_1457905851217_8_26
## - dl_grid_model_1457905851217_8_37
## - dl_grid_model_1457905851217_8_13
## - dl_grid_model_1457905851217_8_35
##
## Number of failed models: 0
model_ids <- grid@model_ids
models <- lapply(model_ids, function(id) { h2o.getModel(id)})
models
## [[1]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_19
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 49,726 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.019901 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.019901 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.019901 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501989 -0.001241 0.249808 0.012369 0.121621
## 3 0.501989 0.000101 0.067576 -0.002646 0.037190
## 4 0.501989 0.013619 0.171743 0.002063 0.264019
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1626578
## R^2: 0.344291
## LogLoss: 0.5367067
## AUC: 0.8648313
## Gini: 0.7296625
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 348 108 0.236842 =108/456
## UP 97 447 0.178309 =97/544
## Totals 445 555 0.205000 =205/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.624929 0.813467 185
## 2 max f2 0.084093 0.883959 334
## 3 max f0point5 0.927394 0.822180 76
## 4 max accuracy 0.639873 0.795000 183
## 5 max precision 0.993634 1.000000 0
## 6 max absolute_MCC 0.639873 0.586236 183
## 7 max min_per_class_accuracy 0.719337 0.789474 161
##
##
##
## [[2]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_24
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 32,934 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.009967 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.009967 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.009967 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009967 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501317 -0.000091 0.112018 0.002275 0.026177
## 3 0.501317 -0.000130 0.045342 -0.000408 0.010156
## 4 0.501317 -0.000146 0.045012 0.000217 0.006742
## 5 0.501317 -0.003293 0.103511 -0.000000 0.133594
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1483592
## R^2: 0.4019318
## LogLoss: 0.4646102
## AUC: 0.8702371
## Gini: 0.7404742
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 332 124 0.271930 =124/456
## UP 78 466 0.143382 =78/544
## Totals 410 590 0.202000 =202/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.477978 0.821869 222
## 2 max f2 0.173218 0.886403 309
## 3 max f0point5 0.790988 0.831911 134
## 4 max accuracy 0.602110 0.799000 192
## 5 max precision 0.982665 1.000000 0
## 6 max absolute_MCC 0.602110 0.594418 192
## 7 max min_per_class_accuracy 0.667235 0.794118 176
##
##
##
## [[3]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_27
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 16,140 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.019968 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.019968 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.019968 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019968 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.500646 0.000291 0.118114 -0.001405 0.036532
## 3 0.500646 -0.000138 0.045313 -0.000065 0.015720
## 4 0.500646 -0.000149 0.044904 0.000042 0.007013
## 5 0.500646 -0.003295 0.104159 0.000000 0.098103
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1543109
## R^2: 0.377939
## LogLoss: 0.4825531
## AUC: 0.862211
## Gini: 0.7244219
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 313 143 0.313596 =143/456
## UP 71 473 0.130515 =71/544
## Totals 384 616 0.214000 =214/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.343151 0.815517 252
## 2 max f2 0.097415 0.885240 342
## 3 max f0point5 0.726334 0.823627 148
## 4 max accuracy 0.555973 0.790000 198
## 5 max precision 0.980575 1.000000 0
## 6 max absolute_MCC 0.649313 0.579397 172
## 7 max min_per_class_accuracy 0.591604 0.787281 189
##
##
##
## [[4]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_8
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 25,694 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.009997 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.009997 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.009997 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009997 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501028 -0.000144 0.112752 0.000013 0.027207
## 3 0.501028 -0.000118 0.045289 0.000332 0.010508
## 4 0.501028 -0.000148 0.044927 -0.000007 0.005385
## 5 0.501028 -0.003294 0.103339 -0.009607 0.129551
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1440823
## R^2: 0.419173
## LogLoss: 0.4494237
## AUC: 0.8708882
## Gini: 0.7417763
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 343 113 0.247807 =113/456
## UP 84 460 0.154412 =84/544
## Totals 427 573 0.197000 =197/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.507066 0.823635 211
## 2 max f2 0.185955 0.891393 316
## 3 max f0point5 0.728470 0.833333 136
## 4 max accuracy 0.507066 0.803000 211
## 5 max precision 0.970150 1.000000 0
## 6 max absolute_MCC 0.507066 0.601912 211
## 7 max min_per_class_accuracy 0.628000 0.796053 179
##
##
##
## [[5]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_15
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 60,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019988 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.019988 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.019988 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019988 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502400 -0.019309 0.328031 0.002037 0.117620
## 3 0.502400 0.000351 0.109422 -0.009580 0.049107
## 4 0.502400 0.000323 0.092832 -0.000041 0.019998
## 5 0.502400 0.014493 0.195558 0.000000 0.194651
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1802678
## R^2: 0.2733014
## LogLoss: 0.5779025
## AUC: 0.8448465
## Gini: 0.689693
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 316 140 0.307018 =140/456
## UP 85 459 0.156250 =85/544
## Totals 401 599 0.225000 =225/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.464885 0.803150 221
## 2 max f2 0.089900 0.882063 379
## 3 max f0point5 0.929821 0.802469 84
## 4 max accuracy 0.811956 0.775000 150
## 5 max precision 0.961231 1.000000 0
## 6 max absolute_MCC 0.811956 0.548659 150
## 7 max min_per_class_accuracy 0.811956 0.773897 150
##
##
##
## [[6]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_43
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 32,604 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.019369 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.019369 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.019369 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019369 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501304 -0.000839 0.129777 0.003506 0.040153
## 3 0.501304 -0.000119 0.044447 0.000090 0.016989
## 4 0.501304 -0.000148 0.043777 0.000385 0.009778
## 5 0.501304 -0.003249 0.103781 0.000330 0.195060
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1570646
## R^2: 0.3668384
## LogLoss: 0.5021792
## AUC: 0.8647647
## Gini: 0.7295295
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 307 149 0.326754 =149/456
## UP 61 483 0.112132 =61/544
## Totals 368 632 0.210000 =210/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.356498 0.821429 244
## 2 max f2 0.095936 0.889716 336
## 3 max f0point5 0.883208 0.822011 100
## 4 max accuracy 0.665637 0.794000 175
## 5 max precision 0.990196 1.000000 0
## 6 max absolute_MCC 0.665637 0.584937 175
## 7 max min_per_class_accuracy 0.715980 0.788603 161
##
##
##
## [[7]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_33
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 38,048 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.009633 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.009633 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.009633 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501522 -0.000015 0.111337 -0.001882 0.014071
## 3 0.501522 0.000319 0.069381 0.000280 0.010010
## 4 0.501522 0.007925 0.113197 0.000000 0.104869
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1638547
## R^2: 0.3394662
## LogLoss: 0.5195897
## AUC: 0.8535398
## Gini: 0.7070796
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 313 143 0.313596 =143/456
## UP 78 466 0.143382 =78/544
## Totals 391 609 0.221000 =221/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.476420 0.808326 228
## 2 max f2 0.097218 0.883148 337
## 3 max f0point5 0.775384 0.816285 139
## 4 max accuracy 0.775384 0.779000 139
## 5 max precision 0.996357 1.000000 0
## 6 max absolute_MCC 0.775384 0.565297 139
## 7 max min_per_class_accuracy 0.689625 0.776316 170
##
##
##
## [[8]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_38
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 35,198 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019320 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.019320 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.019320 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019320 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501408 -0.022578 0.543596 0.015194 0.310810
## 3 0.501408 0.000545 0.081921 0.004647 0.048052
## 4 0.501408 -0.000104 0.074705 -0.000293 0.049426
## 5 0.501408 0.034776 0.221946 -0.006339 0.256919
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1601879
## R^2: 0.3542477
## LogLoss: 0.5043729
## AUC: 0.8550697
## Gini: 0.7101393
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 349 107 0.234649 =107/456
## UP 92 452 0.169118 =92/544
## Totals 441 559 0.199000 =199/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.652859 0.819583 190
## 2 max f2 0.205508 0.885363 350
## 3 max f0point5 0.904901 0.826649 81
## 4 max accuracy 0.652859 0.801000 190
## 5 max precision 0.927970 1.000000 0
## 6 max absolute_MCC 0.652859 0.598099 190
## 7 max min_per_class_accuracy 0.776035 0.792279 161
##
##
##
## [[9]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_12
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 78,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009992 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.009992 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.009992 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009992 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.503120 -0.010517 0.592971 0.013215 0.334167
## 3 0.503120 0.000421 0.144646 0.008034 0.063836
## 4 0.503120 0.000035 0.093813 0.000042 0.030616
## 5 0.503120 0.014526 0.193559 0.001651 0.305373
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1647713
## R^2: 0.3357711
## LogLoss: 0.5396234
## AUC: 0.8626866
## Gini: 0.7253733
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 320 136 0.298246 =136/456
## UP 68 476 0.125000 =68/544
## Totals 388 612 0.204000 =204/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.251860 0.823529 258
## 2 max f2 0.115894 0.885860 369
## 3 max f0point5 0.934725 0.825390 97
## 4 max accuracy 0.430301 0.798000 231
## 5 max precision 0.966775 0.936170 1
## 6 max absolute_MCC 0.430301 0.591892 231
## 7 max min_per_class_accuracy 0.755453 0.787281 174
##
##
##
## [[10]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_22
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 27,277 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019946 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.019946 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.019946 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019946 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501091 -0.012316 0.421154 -0.010069 0.154999
## 3 0.501091 0.000306 0.074092 0.003211 0.032471
## 4 0.501091 -0.000050 0.070647 -0.002242 0.037002
## 5 0.501091 0.034893 0.222972 -0.000000 0.182359
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1591106
## R^2: 0.3585906
## LogLoss: 0.5046268
## AUC: 0.8624206
## Gini: 0.7248412
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 324 132 0.289474 =132/456
## UP 77 467 0.141544 =77/544
## Totals 401 599 0.209000 =209/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.466481 0.817148 221
## 2 max f2 0.159670 0.883063 316
## 3 max f0point5 0.882114 0.821918 102
## 4 max accuracy 0.673500 0.797000 179
## 5 max precision 0.958677 1.000000 0
## 6 max absolute_MCC 0.673500 0.590275 179
## 7 max min_per_class_accuracy 0.757359 0.789474 158
##
##
##
## [[11]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_18
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 103,180 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.019796 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.019796 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.019796 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.504127 -0.007860 0.459964 0.008145 0.412866
## 3 0.504127 0.000041 0.086202 0.012243 0.101060
## 4 0.504127 0.013444 0.167717 0.005278 0.233918
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1506486
## R^2: 0.3927025
## LogLoss: 0.4955137
## AUC: 0.8841327
## Gini: 0.7682654
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 332 124 0.271930 =124/456
## UP 73 471 0.134191 =73/544
## Totals 405 595 0.197000 =197/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.481713 0.827041 205
## 2 max f2 0.065697 0.887803 342
## 3 max f0point5 0.895947 0.854922 96
## 4 max accuracy 0.882916 0.808000 102
## 5 max precision 0.995050 1.000000 0
## 6 max absolute_MCC 0.891412 0.628681 99
## 7 max min_per_class_accuracy 0.722960 0.795956 148
##
##
##
## [[12]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_11
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 25,354 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.019995 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.019995 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.019995 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019995 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501014 0.000197 0.127242 0.000676 0.038729
## 3 0.501014 -0.000126 0.044810 0.000589 0.019292
## 4 0.501014 -0.000149 0.044206 -0.000485 0.009341
## 5 0.501014 -0.003272 0.103404 0.004568 0.120445
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1498031
## R^2: 0.396111
## LogLoss: 0.4653506
## AUC: 0.8629608
## Gini: 0.7259215
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 313 143 0.313596 =143/456
## UP 69 475 0.126838 =69/544
## Totals 382 618 0.212000 =212/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.431493 0.817556 236
## 2 max f2 0.159043 0.885734 331
## 3 max f0point5 0.681070 0.821372 158
## 4 max accuracy 0.647639 0.795000 169
## 5 max precision 0.964970 1.000000 0
## 6 max absolute_MCC 0.647639 0.589913 169
## 7 max min_per_class_accuracy 0.632264 0.794118 176
##
##
##
## [[13]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_46
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 54,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.018975 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.018975 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.018975 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.018975 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502160 -0.017688 0.731784 0.063544 0.464064
## 3 0.502160 -0.000063 0.191040 -0.006833 0.124393
## 4 0.502160 0.000322 0.096922 -0.007678 0.055996
## 5 0.502160 0.006844 0.162088 0.009850 0.185192
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1725589
## R^2: 0.3043773
## LogLoss: 0.5528586
## AUC: 0.8322892
## Gini: 0.6645785
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 320 136 0.298246 =136/456
## UP 77 467 0.141544 =77/544
## Totals 397 603 0.213000 =213/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.329443 0.814298 246
## 2 max f2 0.116535 0.878057 367
## 3 max f0point5 0.860166 0.805085 156
## 4 max accuracy 0.657481 0.789000 198
## 5 max precision 0.939729 0.868805 42
## 6 max absolute_MCC 0.657481 0.574017 198
## 7 max min_per_class_accuracy 0.821158 0.778509 168
##
##
##
## [[14]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_4
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 42,705 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009996 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.009996 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.009996 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009996 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501708 -0.007007 0.489841 0.022898 0.271116
## 3 0.501708 0.000464 0.077390 0.002201 0.030772
## 4 0.501708 -0.000072 0.070950 -0.000540 0.030440
## 5 0.501708 0.034765 0.219779 -0.008417 0.217558
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1517823
## R^2: 0.3881324
## LogLoss: 0.4864908
## AUC: 0.8777493
## Gini: 0.7554986
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 344 112 0.245614 =112/456
## UP 81 463 0.148897 =81/544
## Totals 425 575 0.193000 =193/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.475785 0.827525 220
## 2 max f2 0.116159 0.887236 332
## 3 max f0point5 0.889513 0.845218 121
## 4 max accuracy 0.837014 0.809000 146
## 5 max precision 0.963971 1.000000 0
## 6 max absolute_MCC 0.837014 0.622098 146
## 7 max min_per_class_accuracy 0.726068 0.799632 178
##
##
##
## [[15]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_16
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 95,536 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.009905 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.009905 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.009905 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.503821 -0.004590 0.232665 0.013103 0.154397
## 3 0.503821 0.000355 0.068312 0.000497 0.035599
## 4 0.503821 0.013785 0.169529 -0.002265 0.322088
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1467153
## R^2: 0.4085587
## LogLoss: 0.4642349
## AUC: 0.8779952
## Gini: 0.7559904
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 336 120 0.263158 =120/456
## UP 77 467 0.141544 =77/544
## Totals 413 587 0.197000 =197/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.507021 0.825818 214
## 2 max f2 0.140230 0.887152 321
## 3 max f0point5 0.830500 0.843271 120
## 4 max accuracy 0.783344 0.804000 138
## 5 max precision 0.990602 1.000000 0
## 6 max absolute_MCC 0.783344 0.613946 138
## 7 max min_per_class_accuracy 0.678521 0.798246 170
##
##
##
## [[16]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_25
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 25,056 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.009975 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.009975 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.009975 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009975 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501002 0.000301 0.093910 -0.000755 0.015486
## 3 0.501002 -0.000119 0.046047 -0.000359 0.007668
## 4 0.501002 -0.000152 0.045777 -0.000446 0.006531
## 5 0.501002 -0.002082 0.065352 -0.000000 0.124258
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.153262
## R^2: 0.3821674
## LogLoss: 0.4728607
## AUC: 0.8593629
## Gini: 0.7187258
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 300 156 0.342105 =156/456
## UP 61 483 0.112132 =61/544
## Totals 361 639 0.217000 =217/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.426005 0.816568 241
## 2 max f2 0.145877 0.885762 347
## 3 max f0point5 0.781258 0.814261 126
## 4 max accuracy 0.613774 0.788000 187
## 5 max precision 0.970464 1.000000 0
## 6 max absolute_MCC 0.613774 0.572406 187
## 7 max min_per_class_accuracy 0.669882 0.784926 172
##
##
##
## [[17]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_23
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 43,565 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019913 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.019913 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.019913 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019913 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501743 0.026653 0.486142 0.003011 0.247115
## 3 0.501743 0.000514 0.076556 0.001091 0.046980
## 4 0.501743 0.000073 0.071558 0.005225 0.050659
## 5 0.501743 0.034771 0.222714 0.004242 0.230683
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.173379
## R^2: 0.3010714
## LogLoss: 0.5766516
## AUC: 0.8568353
## Gini: 0.7136707
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 326 130 0.285088 =130/456
## UP 80 464 0.147059 =80/544
## Totals 406 594 0.210000 =210/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.398586 0.815466 222
## 2 max f2 0.080227 0.882453 352
## 3 max f0point5 0.894103 0.826104 131
## 4 max accuracy 0.894103 0.796000 131
## 5 max precision 0.973969 0.938462 3
## 6 max absolute_MCC 0.894103 0.594664 131
## 7 max min_per_class_accuracy 0.833823 0.779412 154
##
##
##
## [[18]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_1
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 60,030 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.009994 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.009994 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.009994 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502401 -0.002312 0.186890 0.000629 0.101840
## 3 0.502401 0.000339 0.067567 0.000232 0.026308
## 4 0.502401 0.013836 0.170846 0.002280 0.309615
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1576219
## R^2: 0.3645917
## LogLoss: 0.5000199
## AUC: 0.8668126
## Gini: 0.7336252
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 315 141 0.309211 =141/456
## UP 72 472 0.132353 =72/544
## Totals 387 613 0.213000 =213/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.460468 0.815903 220
## 2 max f2 0.080929 0.884603 351
## 3 max f0point5 0.878813 0.831105 99
## 4 max accuracy 0.723686 0.797000 150
## 5 max precision 0.991862 1.000000 0
## 6 max absolute_MCC 0.723686 0.593385 150
## 7 max min_per_class_accuracy 0.707524 0.794118 154
##
##
##
## [[19]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_9
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 16,805 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.009998 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.009998 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.009998 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009998 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.500672 0.000064 0.094250 -0.001274 0.018597
## 3 0.500672 -0.000141 0.045908 0.000413 0.009603
## 4 0.500672 -0.000157 0.045760 -0.000192 0.008038
## 5 0.500672 -0.003330 0.105658 -0.000025 0.119857
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1604394
## R^2: 0.353234
## LogLoss: 0.4979507
## AUC: 0.8587038
## Gini: 0.7174076
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 286 170 0.372807 =170/456
## UP 58 486 0.106618 =58/544
## Totals 344 656 0.228000 =228/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.351291 0.810000 259
## 2 max f2 0.103772 0.884218 344
## 3 max f0point5 0.768000 0.821429 145
## 4 max accuracy 0.761827 0.791000 148
## 5 max precision 0.987989 1.000000 0
## 6 max absolute_MCC 0.761827 0.584091 148
## 7 max min_per_class_accuracy 0.705000 0.787281 167
##
##
##
## [[20]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_36
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 35,198 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009660 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.009660 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.009660 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009660 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501408 -0.002104 0.437015 -0.002230 0.229438
## 3 0.501408 0.000507 0.075321 0.000225 0.024598
## 4 0.501408 0.000120 0.070200 -0.003835 0.026060
## 5 0.501408 0.034819 0.219865 -0.000000 0.249070
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1509604
## R^2: 0.3914457
## LogLoss: 0.478935
## AUC: 0.8743429
## Gini: 0.7486858
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 336 120 0.263158 =120/456
## UP 76 468 0.139706 =76/544
## Totals 412 588 0.196000 =196/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.408951 0.826855 237
## 2 max f2 0.133417 0.886986 344
## 3 max f0point5 0.872993 0.842483 122
## 4 max accuracy 0.408951 0.804000 237
## 5 max precision 0.961425 1.000000 0
## 6 max absolute_MCC 0.841574 0.610831 138
## 7 max min_per_class_accuracy 0.670065 0.798246 181
##
##
##
## [[21]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_39
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 35,198 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019320 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.019320 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.019320 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019320 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501408 -0.001232 0.499727 0.013557 0.242467
## 3 0.501408 0.000697 0.078178 0.000707 0.042336
## 4 0.501408 0.000221 0.072987 0.005057 0.045531
## 5 0.501408 0.025498 0.194713 -0.007165 0.210428
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1703277
## R^2: 0.3133718
## LogLoss: 0.5386076
## AUC: 0.8512985
## Gini: 0.7025969
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 340 116 0.254386 =116/456
## UP 96 448 0.176471 =96/544
## Totals 436 564 0.212000 =212/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.619251 0.808664 198
## 2 max f2 0.134722 0.882353 346
## 3 max f0point5 0.909962 0.816413 104
## 4 max accuracy 0.646314 0.788000 196
## 5 max precision 0.945883 1.000000 0
## 6 max absolute_MCC 0.646314 0.571701 196
## 7 max min_per_class_accuracy 0.786800 0.780702 162
##
##
##
## [[22]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_44
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 78,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009276 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.009276 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.009276 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009276 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.503120 -0.014628 0.445644 0.023992 0.180828
## 3 0.503120 0.000110 0.122202 -0.002477 0.060277
## 4 0.503120 0.000320 0.093474 -0.003678 0.024477
## 5 0.503120 0.014479 0.195138 0.000524 0.238568
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.168018
## R^2: 0.3226827
## LogLoss: 0.5481999
## AUC: 0.8584922
## Gini: 0.7169843
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 324 132 0.289474 =132/456
## UP 76 468 0.139706 =76/544
## Totals 400 600 0.208000 =208/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.331712 0.818182 225
## 2 max f2 0.104276 0.881757 357
## 3 max f0point5 0.928712 0.829187 101
## 4 max accuracy 0.786646 0.794000 159
## 5 max precision 0.964956 1.000000 0
## 6 max absolute_MCC 0.928712 0.587362 101
## 7 max min_per_class_accuracy 0.821594 0.787281 154
##
##
##
## [[23]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_45
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 78,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009276 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.009276 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.009276 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009276 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.503120 -0.046467 0.592000 -0.001315 0.321958
## 3 0.503120 -0.001237 0.148049 0.003970 0.070714
## 4 0.503120 0.000267 0.093688 -0.003360 0.028881
## 5 0.503120 0.014472 0.192572 0.004852 0.226244
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1697848
## R^2: 0.3155603
## LogLoss: 0.5463388
## AUC: 0.8559666
## Gini: 0.7119332
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 309 147 0.322368 =147/456
## UP 67 477 0.123162 =67/544
## Totals 376 624 0.214000 =214/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.210996 0.816781 278
## 2 max f2 0.123385 0.883642 390
## 3 max f0point5 0.923767 0.817953 115
## 4 max accuracy 0.737426 0.790000 193
## 5 max precision 0.949928 0.920000 1
## 6 max absolute_MCC 0.737426 0.577214 193
## 7 max min_per_class_accuracy 0.797093 0.785088 181
##
##
##
## [[24]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_31
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 60,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019881 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.019881 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.019881 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019881 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502400 -0.008616 0.335211 0.005017 0.131337
## 3 0.502400 0.000702 0.110490 -0.005770 0.056900
## 4 0.502400 0.000821 0.092804 -0.007438 0.019982
## 5 0.502400 0.006611 0.162674 -0.000570 0.095912
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1793299
## R^2: 0.2770823
## LogLoss: 0.5714629
## AUC: 0.841809
## Gini: 0.6836179
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 297 159 0.348684 =159/456
## UP 71 473 0.130515 =71/544
## Totals 368 632 0.230000 =230/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.343142 0.804422 252
## 2 max f2 0.084971 0.879959 355
## 3 max f0point5 0.921189 0.798923 79
## 4 max accuracy 0.629063 0.776000 194
## 5 max precision 0.957669 1.000000 0
## 6 max absolute_MCC 0.629063 0.547131 194
## 7 max min_per_class_accuracy 0.781244 0.763158 149
##
##
##
## [[25]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_32
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 68,418 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.009360 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.009360 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.009360 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502737 -0.000533 0.193340 0.006494 0.096838
## 3 0.502737 0.000322 0.068723 0.000833 0.025193
## 4 0.502737 0.013890 0.171380 -0.000067 0.211800
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1497868
## R^2: 0.3961768
## LogLoss: 0.476127
## AUC: 0.8738894
## Gini: 0.7477788
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 340 116 0.254386 =116/456
## UP 82 462 0.150735 =82/544
## Totals 422 578 0.198000 =198/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.500601 0.823529 213
## 2 max f2 0.050227 0.884970 366
## 3 max f0point5 0.813734 0.844652 129
## 4 max accuracy 0.500601 0.802000 213
## 5 max precision 0.992143 1.000000 0
## 6 max absolute_MCC 0.813734 0.611340 129
## 7 max min_per_class_accuracy 0.645562 0.793860 178
##
##
##
## [[26]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_42
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 28,963 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.019437 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.019437 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.019437 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019437 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501159 0.000410 0.173602 -0.003464 0.087014
## 3 0.501159 -0.000122 0.043874 0.000515 0.020670
## 4 0.501159 -0.000128 0.042777 0.000075 0.009551
## 5 0.501159 -0.003210 0.102858 -0.002871 0.186586
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1475605
## R^2: 0.4051516
## LogLoss: 0.4615381
## AUC: 0.8739821
## Gini: 0.7479642
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 301 155 0.339912 =155/456
## UP 57 487 0.104779 =57/544
## Totals 358 642 0.212000 =212/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.331632 0.821248 260
## 2 max f2 0.155194 0.883569 316
## 3 max f0point5 0.808141 0.839844 127
## 4 max accuracy 0.771416 0.798000 142
## 5 max precision 0.984528 1.000000 0
## 6 max absolute_MCC 0.771416 0.605216 142
## 7 max min_per_class_accuracy 0.655982 0.793860 178
##
##
##
## [[27]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_41
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 43,784 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.009581 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.009581 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.009581 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009581 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501751 -0.000865 0.112472 0.002311 0.046905
## 3 0.501751 -0.000145 0.044725 0.000356 0.012833
## 4 0.501751 -0.000146 0.044287 -0.000315 0.005286
## 5 0.501751 -0.003271 0.103187 0.001227 0.121036
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1467692
## R^2: 0.4083415
## LogLoss: 0.4583967
## AUC: 0.8675705
## Gini: 0.7351409
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 361 95 0.208333 =95/456
## UP 100 444 0.183824 =100/544
## Totals 461 539 0.195000 =195/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.571901 0.819945 199
## 2 max f2 0.121704 0.885135 338
## 3 max f0point5 0.607935 0.831422 188
## 4 max accuracy 0.607935 0.808000 188
## 5 max precision 0.978624 1.000000 0
## 6 max absolute_MCC 0.607935 0.615618 188
## 7 max min_per_class_accuracy 0.596209 0.804825 192
##
##
##
## [[28]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_10
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 25,354 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.019995 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.019995 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.019995 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019995 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501014 0.001021 0.159039 -0.001734 0.058300
## 3 0.501014 -0.000139 0.044215 -0.000231 0.023359
## 4 0.501014 -0.000164 0.043209 0.000679 0.011522
## 5 0.501014 -0.002221 0.071740 0.001223 0.204515
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1595327
## R^2: 0.356889
## LogLoss: 0.5579547
## AUC: 0.8710333
## Gini: 0.7420666
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 323 133 0.291667 =133/456
## UP 69 475 0.126838 =69/544
## Totals 392 608 0.202000 =202/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.359766 0.824653 239
## 2 max f2 0.047279 0.888889 342
## 3 max f0point5 0.930767 0.833333 88
## 4 max accuracy 0.496429 0.800000 211
## 5 max precision 0.997995 1.000000 0
## 6 max absolute_MCC 0.489367 0.595832 214
## 7 max min_per_class_accuracy 0.703153 0.793860 170
##
##
##
## [[29]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_34
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 60,813 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.018853 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.018853 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.018853 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502432 -0.000778 0.342567 -0.004697 0.257620
## 3 0.502432 0.000247 0.071081 0.000441 0.057294
## 4 0.502432 0.013712 0.171797 -0.018204 0.194183
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1580248
## R^2: 0.3629676
## LogLoss: 0.51412
## AUC: 0.8661293
## Gini: 0.7322586
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 302 154 0.337719 =154/456
## UP 62 482 0.113971 =62/544
## Totals 364 636 0.216000 =216/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.272495 0.816949 260
## 2 max f2 0.038328 0.880039 373
## 3 max f0point5 0.885274 0.832585 104
## 4 max accuracy 0.727332 0.798000 158
## 5 max precision 0.987921 1.000000 0
## 6 max absolute_MCC 0.727332 0.597634 158
## 7 max min_per_class_accuracy 0.653053 0.789474 174
##
##
##
## [[30]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_47
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 60,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.018868 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.018868 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.018868 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.018868 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502400 -0.022736 0.342429 0.010774 0.102840
## 3 0.502400 0.001092 0.110536 0.008491 0.041514
## 4 0.502400 0.000663 0.092985 -0.001013 0.019302
## 5 0.502400 0.008202 0.119529 0.000000 0.115061
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1829986
## R^2: 0.2622928
## LogLoss: 0.5976631
## AUC: 0.8444615
## Gini: 0.688923
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 307 149 0.326754 =149/456
## UP 77 467 0.141544 =77/544
## Totals 384 616 0.226000 =226/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.275746 0.805172 250
## 2 max f2 0.059586 0.880786 377
## 3 max f0point5 0.927939 0.802336 99
## 4 max accuracy 0.759831 0.779000 174
## 5 max precision 0.964679 1.000000 0
## 6 max absolute_MCC 0.759831 0.555351 174
## 7 max min_per_class_accuracy 0.804035 0.770221 164
##
##
##
## [[31]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_28
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 78,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009923 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.009923 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.009923 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009923 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.503120 -0.020678 0.433392 0.008388 0.154900
## 3 0.503120 0.000662 0.119402 -0.008209 0.051403
## 4 0.503120 0.000512 0.093283 -0.001823 0.025512
## 5 0.503120 0.014529 0.194474 -0.005560 0.235439
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.166087
## R^2: 0.3304671
## LogLoss: 0.540576
## AUC: 0.8637469
## Gini: 0.7274937
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 326 130 0.285088 =130/456
## UP 75 469 0.137868 =75/544
## Totals 401 599 0.205000 =205/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.415609 0.820647 216
## 2 max f2 0.106770 0.885773 339
## 3 max f0point5 0.895627 0.830632 114
## 4 max accuracy 0.895627 0.795000 114
## 5 max precision 0.969429 1.000000 0
## 6 max absolute_MCC 0.895627 0.596085 114
## 7 max min_per_class_accuracy 0.821074 0.788603 147
##
##
##
## [[32]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_2
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 37,506 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.019993 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501500 -0.004589 0.222594 -0.003893 0.101513
## 3 0.501500 0.000425 0.068504 0.000947 0.027207
## 4 0.501500 0.013807 0.174442 -0.000000 0.262489
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1581111
## R^2: 0.3626198
## LogLoss: 0.4972461
## AUC: 0.8621122
## Gini: 0.7242244
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 300 156 0.342105 =156/456
## UP 61 483 0.112132 =61/544
## Totals 361 639 0.217000 =217/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.383185 0.816568 246
## 2 max f2 0.095099 0.879121 351
## 3 max f0point5 0.823422 0.827703 122
## 4 max accuracy 0.779221 0.788000 140
## 5 max precision 0.984935 1.000000 0
## 6 max absolute_MCC 0.823422 0.580237 122
## 7 max min_per_class_accuracy 0.686865 0.781250 163
##
##
##
## [[33]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_7
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 35,449 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019993 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501418 -0.014674 0.664914 0.116600 0.477172
## 3 0.501418 0.000451 0.094001 0.004636 0.062352
## 4 0.501418 0.000360 0.082317 -0.000567 0.079174
## 5 0.501418 0.019655 0.190907 -0.007378 0.205828
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1624834
## R^2: 0.3449942
## LogLoss: 0.5075857
## AUC: 0.8457999
## Gini: 0.6915997
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 315 141 0.309211 =141/456
## UP 70 474 0.128676 =70/544
## Totals 385 615 0.211000 =211/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.296537 0.817947 266
## 2 max f2 0.185794 0.883157 361
## 3 max f0point5 0.847823 0.817776 137
## 4 max accuracy 0.675696 0.791000 185
## 5 max precision 0.914198 0.900000 11
## 6 max absolute_MCC 0.675696 0.579700 185
## 7 max min_per_class_accuracy 0.704114 0.785088 178
##
##
##
## [[34]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_29
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 78,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009923 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.009923 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.009923 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009923 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.503120 -0.009091 0.417038 -0.007334 0.190441
## 3 0.503120 0.000463 0.119018 0.009932 0.058935
## 4 0.503120 0.000390 0.092972 -0.000255 0.024646
## 5 0.503120 0.014515 0.194747 -0.000187 0.199444
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1698062
## R^2: 0.3154742
## LogLoss: 0.5526462
## AUC: 0.8580769
## Gini: 0.7161539
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 319 137 0.300439 =137/456
## UP 77 467 0.141544 =77/544
## Totals 396 604 0.214000 =214/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.287574 0.813589 239
## 2 max f2 0.098964 0.883562 352
## 3 max f0point5 0.904030 0.815747 121
## 4 max accuracy 0.538939 0.791000 198
## 5 max precision 0.961005 1.000000 0
## 6 max absolute_MCC 0.816811 0.579454 155
## 7 max min_per_class_accuracy 0.800863 0.788603 160
##
##
##
## [[35]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_21
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 51,698 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009949 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.009949 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.009949 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009949 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502068 0.013893 0.375206 0.000211 0.180452
## 3 0.502068 0.000508 0.072501 -0.000549 0.021351
## 4 0.502068 -0.000073 0.069600 -0.002541 0.020502
## 5 0.502068 0.034864 0.219382 -0.001530 0.240921
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1583589
## R^2: 0.3616208
## LogLoss: 0.4996875
## AUC: 0.8658008
## Gini: 0.7316015
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 319 137 0.300439 =137/456
## UP 76 468 0.139706 =76/544
## Totals 395 605 0.213000 =213/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.332737 0.814621 249
## 2 max f2 0.137541 0.885773 346
## 3 max f0point5 0.893742 0.827526 109
## 4 max accuracy 0.785499 0.797000 157
## 5 max precision 0.955918 1.000000 0
## 6 max absolute_MCC 0.785499 0.596138 157
## 7 max min_per_class_accuracy 0.711005 0.793860 177
##
##
##
## [[36]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_3
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 101,316 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.019980 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.019980 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.019980 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.504053 -0.001461 0.320524 0.007884 0.232922
## 3 0.504053 0.000125 0.070737 -0.001347 0.051872
## 4 0.504053 0.013509 0.166921 -0.028067 0.236425
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1580222
## R^2: 0.3629781
## LogLoss: 0.5141486
## AUC: 0.8708116
## Gini: 0.7416231
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 339 117 0.256579 =117/456
## UP 88 456 0.161765 =88/544
## Totals 427 573 0.205000 =205/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.459821 0.816473 216
## 2 max f2 0.037232 0.886724 370
## 3 max f0point5 0.824906 0.833333 131
## 4 max accuracy 0.664951 0.796000 177
## 5 max precision 0.992127 1.000000 0
## 6 max absolute_MCC 0.824906 0.594177 131
## 7 max min_per_class_accuracy 0.664951 0.794118 177
##
##
##
## [[37]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_20
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 35,434 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009965 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.009965 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.009965 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009965 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501417 -0.009239 0.329782 -0.005930 0.124079
## 3 0.501417 0.000637 0.072196 0.000935 0.021148
## 4 0.501417 0.000255 0.069747 -0.001938 0.023578
## 5 0.501417 0.034926 0.220726 -0.000000 0.235259
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.15852
## R^2: 0.3609712
## LogLoss: 0.511501
## AUC: 0.8697755
## Gini: 0.7395511
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 331 125 0.274123 =125/456
## UP 76 468 0.139706 =76/544
## Totals 407 593 0.201000 =201/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.448600 0.823219 215
## 2 max f2 0.104005 0.885061 340
## 3 max f0point5 0.914597 0.836938 99
## 4 max accuracy 0.706395 0.799000 166
## 5 max precision 0.976149 1.000000 0
## 6 max absolute_MCC 0.885111 0.599711 117
## 7 max min_per_class_accuracy 0.752769 0.794118 155
##
##
##
## [[38]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_6
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 34,714 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.019993 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019993 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501389 -0.005960 0.413470 0.001825 0.195113
## 3 0.501389 0.000597 0.074182 -0.002700 0.031141
## 4 0.501389 0.000023 0.070659 -0.001375 0.032502
## 5 0.501389 0.034920 0.222592 -0.000000 0.298186
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1697125
## R^2: 0.315852
## LogLoss: 0.5674432
## AUC: 0.8592783
## Gini: 0.7185565
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 333 123 0.269737 =123/456
## UP 82 462 0.150735 =82/544
## Totals 415 585 0.205000 =205/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.436481 0.818423 210
## 2 max f2 0.084577 0.880258 350
## 3 max f0point5 0.940330 0.831174 90
## 4 max accuracy 0.461980 0.795000 205
## 5 max precision 0.980934 0.937500 0
## 6 max absolute_MCC 0.436481 0.585807 210
## 7 max min_per_class_accuracy 0.782302 0.791667 155
##
##
##
## [[39]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_40
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 31,268 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.009697 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.009697 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.009697 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009697 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501251 0.000330 0.107060 -0.001394 0.020019
## 3 0.501251 -0.000112 0.045846 0.000618 0.008127
## 4 0.501251 -0.000153 0.045354 -0.000153 0.005183
## 5 0.501251 -0.002379 0.071743 -0.006795 0.108193
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1477379
## R^2: 0.4044363
## LogLoss: 0.4611632
## AUC: 0.8697272
## Gini: 0.7394543
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 332 124 0.271930 =124/456
## UP 77 467 0.141544 =77/544
## Totals 409 591 0.201000 =201/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.504961 0.822907 219
## 2 max f2 0.148529 0.887262 322
## 3 max f0point5 0.816842 0.837912 106
## 4 max accuracy 0.520948 0.799000 212
## 5 max precision 0.980875 1.000000 0
## 6 max absolute_MCC 0.678347 0.595264 167
## 7 max min_per_class_accuracy 0.663442 0.795956 171
##
##
##
## [[40]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_5
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 50,711 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009995 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.009995 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.009995 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009995 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502028 -0.002185 0.378752 0.016803 0.155993
## 3 0.502028 0.000528 0.073082 0.001897 0.025055
## 4 0.502028 0.000075 0.069863 0.001789 0.026297
## 5 0.502028 0.034851 0.219681 -0.008052 0.251050
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1582524
## R^2: 0.36205
## LogLoss: 0.4985361
## AUC: 0.8633276
## Gini: 0.7266552
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 328 128 0.280702 =128/456
## UP 79 465 0.145221 =79/544
## Totals 407 593 0.207000 =207/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.411887 0.817942 223
## 2 max f2 0.144191 0.884387 376
## 3 max f0point5 0.788377 0.825581 149
## 4 max accuracy 0.777294 0.800000 151
## 5 max precision 0.948383 1.000000 0
## 6 max absolute_MCC 0.777294 0.600098 151
## 7 max min_per_class_accuracy 0.722280 0.793860 163
##
##
##
## [[41]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_30
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 60,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019881 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.019881 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.019881 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019881 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502400 0.012785 0.706004 0.028165 0.419705
## 3 0.502400 -0.002535 0.184443 0.013560 0.125341
## 4 0.502400 0.000560 0.097959 -0.008628 0.053093
## 5 0.502400 0.014449 0.195677 0.000000 0.179751
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1781256
## R^2: 0.281937
## LogLoss: 0.6019819
## AUC: 0.8437319
## Gini: 0.6874637
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 310 146 0.320175 =146/456
## UP 70 474 0.128676 =70/544
## Totals 380 620 0.216000 =216/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.292745 0.814433 260
## 2 max f2 0.062142 0.884034 385
## 3 max f0point5 0.833937 0.806452 157
## 4 max accuracy 0.483232 0.788000 222
## 5 max precision 0.970005 1.000000 0
## 6 max absolute_MCC 0.483232 0.571493 222
## 7 max min_per_class_accuracy 0.805569 0.780702 167
##
##
##
## [[42]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_0
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 71,314 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.009993 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.009993 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.009993 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502853 -0.006274 0.226727 0.006884 0.139376
## 3 0.502853 0.000312 0.068081 -0.000401 0.031713
## 4 0.502853 0.013771 0.170350 -0.005806 0.224348
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1478449
## R^2: 0.4040051
## LogLoss: 0.4683478
## AUC: 0.8794021
## Gini: 0.7588042
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 315 141 0.309211 =141/456
## UP 65 479 0.119485 =65/544
## Totals 380 620 0.206000 =206/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.389473 0.823024 246
## 2 max f2 0.095350 0.890248 338
## 3 max f0point5 0.822990 0.849057 129
## 4 max accuracy 0.778766 0.805000 147
## 5 max precision 0.993904 1.000000 0
## 6 max absolute_MCC 0.815349 0.618286 132
## 7 max min_per_class_accuracy 0.666536 0.793860 178
##
##
##
## [[43]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_17
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 61,196 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.009939 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.009939 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.009939 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502448 -0.001229 0.145963 0.004735 0.048078
## 3 0.502448 0.000319 0.068633 0.000415 0.015499
## 4 0.502448 0.007672 0.103916 -0.001252 0.209846
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1583713
## R^2: 0.3615708
## LogLoss: 0.4974161
## AUC: 0.8628358
## Gini: 0.7256716
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 345 111 0.243421 =111/456
## UP 97 447 0.178309 =97/544
## Totals 442 558 0.208000 =208/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.607640 0.811252 188
## 2 max f2 0.106845 0.884836 340
## 3 max f0point5 0.804654 0.825083 128
## 4 max accuracy 0.613969 0.792000 186
## 5 max precision 0.989134 1.000000 0
## 6 max absolute_MCC 0.804654 0.580574 128
## 7 max min_per_class_accuracy 0.690629 0.787281 163
##
##
##
## [[44]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_14
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 60,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.019988 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.019988 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.019988 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019988 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502400 -0.009948 0.357332 -0.008261 0.158015
## 3 0.502400 0.000364 0.112145 -0.001479 0.060348
## 4 0.502400 0.000452 0.093119 -0.001465 0.024627
## 5 0.502400 0.014561 0.196793 0.005197 0.165826
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1822611
## R^2: 0.2652659
## LogLoss: 0.6033105
## AUC: 0.8469387
## Gini: 0.6938774
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 321 135 0.296053 =135/456
## UP 83 461 0.152574 =83/544
## Totals 404 596 0.218000 =218/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.529947 0.808772 208
## 2 max f2 0.068873 0.881861 365
## 3 max f0point5 0.914533 0.803429 111
## 4 max accuracy 0.626679 0.782000 196
## 5 max precision 0.970312 1.000000 0
## 6 max absolute_MCC 0.529947 0.559647 208
## 7 max min_per_class_accuracy 0.847519 0.774123 142
##
##
##
## [[45]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_26
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 511,502 weights/biases, 3.9 MB, 24,414 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 0.00 %
## 2 2 500 TanhDropout 50.00 % 0.000010 0.000010 0.019951 0.000000
## 3 3 500 TanhDropout 50.00 % 0.000010 0.000010 0.019951 0.000000
## 4 4 500 TanhDropout 50.00 % 0.000010 0.000010 0.019951 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.019951 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.500977 -0.000888 0.137980 0.000567 0.036070
## 3 0.500977 -0.000120 0.045072 -0.000396 0.015557
## 4 0.500977 -0.000144 0.044372 0.000069 0.008838
## 5 0.500977 -0.001800 0.061937 0.003211 0.161180
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1572172
## R^2: 0.3662232
## LogLoss: 0.5177955
## AUC: 0.870991
## Gini: 0.7419819
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 325 131 0.287281 =131/456
## UP 73 471 0.134191 =73/544
## Totals 398 602 0.204000 =204/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.317053 0.821990 255
## 2 max f2 0.058175 0.887179 345
## 3 max f0point5 0.887752 0.833333 103
## 4 max accuracy 0.454482 0.798000 223
## 5 max precision 0.993400 1.000000 0
## 6 max absolute_MCC 0.779720 0.598813 154
## 7 max min_per_class_accuracy 0.656022 0.786765 185
##
##
##
## [[46]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_37
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 62,502 weights/biases, 498.1 KB, 35,198 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009660 0.000000
## 3 3 300 TanhDropout 50.00 % 0.000010 0.000010 0.009660 0.000000
## 4 4 100 TanhDropout 50.00 % 0.000010 0.000010 0.009660 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009660 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.501408 -0.000825 0.379402 0.009738 0.184564
## 3 0.501408 0.000469 0.073418 0.003138 0.024328
## 4 0.501408 0.000052 0.070064 -0.005376 0.025090
## 5 0.501408 0.034866 0.218840 0.000501 0.249063
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1547744
## R^2: 0.3760708
## LogLoss: 0.4879292
## AUC: 0.8672439
## Gini: 0.7344879
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 339 117 0.256579 =117/456
## UP 87 457 0.159926 =87/544
## Totals 426 574 0.204000 =204/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.443956 0.817531 221
## 2 max f2 0.161765 0.886598 334
## 3 max f0point5 0.806223 0.829984 143
## 4 max accuracy 0.665209 0.799000 175
## 5 max precision 0.953715 1.000000 0
## 6 max absolute_MCC 0.806223 0.597013 143
## 7 max min_per_class_accuracy 0.665209 0.797794 175
##
##
##
## [[47]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_13
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 27,452 weights/biases, 223.1 KB, 60,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 100 TanhDropout 50.00 % 0.000010 0.000010 0.009994 0.000000
## 3 3 100 TanhDropout 50.00 % 0.000010 0.000010 0.009994 0.000000
## 4 4 150 TanhDropout 50.00 % 0.000010 0.000010 0.009994 0.000000
## 5 5 2 Softmax 0.000010 0.000010 0.009994 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502400 -0.003168 0.406608 0.008546 0.143902
## 3 0.502400 0.000408 0.118265 -0.005543 0.044146
## 4 0.502400 0.000236 0.092947 0.000087 0.023300
## 5 0.502400 0.008255 0.116286 -0.007120 0.204662
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1719011
## R^2: 0.3070292
## LogLoss: 0.5648556
## AUC: 0.8555977
## Gini: 0.7111955
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 330 126 0.276316 =126/456
## UP 83 461 0.152574 =83/544
## Totals 413 587 0.209000 =209/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.401101 0.815208 223
## 2 max f2 0.087869 0.883055 355
## 3 max f0point5 0.924140 0.818106 111
## 4 max accuracy 0.401101 0.791000 223
## 5 max precision 0.966309 1.000000 0
## 6 max absolute_MCC 0.803280 0.578448 166
## 7 max min_per_class_accuracy 0.803280 0.789474 166
##
##
##
## [[48]]
## Model Details:
## ==============
##
## H2OBinomialModel: deeplearning
## Model ID: dl_grid_model_1457905851217_8_35
## Status of Neuron Layers: predicting Class, 2-class classification, bernoulli distribution, CrossEntropy loss, 44,402 weights/biases, 355.3 KB, 72,224 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_RMS
## 1 1 18 Input 5.00 %
## 2 2 200 TanhDropout 50.00 % 0.000010 0.000010 0.018653 0.000000
## 3 3 200 TanhDropout 50.00 % 0.000010 0.000010 0.018653 0.000000
## 4 4 2 Softmax 0.000010 0.000010 0.018653 0.000000
## momentum mean_weight weight_RMS mean_bias bias_RMS
## 1
## 2 0.502889 0.000825 0.220521 0.007287 0.117229
## 3 0.502889 0.000357 0.067448 -0.004574 0.034544
## 4 0.502889 0.010720 0.140193 -0.001015 0.143632
##
##
## H2OBinomialMetrics: deeplearning
## ** Reported on training data. **
## Description: Metrics reported on temporary (load-balanced) training frame
##
## MSE: 0.1557257
## R^2: 0.3722358
## LogLoss: 0.4854518
## AUC: 0.8619106
## Gini: 0.7238213
##
## Confusion Matrix for F1-optimal threshold:
## DOWN UP Error Rate
## DOWN 331 125 0.274123 =125/456
## UP 85 459 0.156250 =85/544
## Totals 416 584 0.210000 =210/1000
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.508937 0.813830 211
## 2 max f2 0.120745 0.883745 337
## 3 max f0point5 0.830979 0.820730 111
## 4 max accuracy 0.663792 0.794000 168
## 5 max precision 0.976455 1.000000 0
## 6 max absolute_MCC 0.663792 0.586726 168
## 7 max min_per_class_accuracy 0.663792 0.792279 168
library(nnet)
nn <- nnet(Class ~ ., data =TrainingSet, size = 2, maxit = 200)
## # weights: 41
## initial value 698.326213
## iter 10 value 522.644052
## iter 20 value 455.995328
## iter 30 value 447.088181
## iter 40 value 439.559102
## iter 50 value 434.328688
## iter 60 value 431.843275
## iter 70 value 430.508417
## iter 80 value 429.779245
## iter 90 value 429.550510
## iter 100 value 428.979007
## iter 110 value 426.460252
## iter 120 value 424.066559
## iter 130 value 423.152778
## iter 140 value 422.200419
## iter 150 value 421.838685
## iter 160 value 421.337656
## iter 170 value 421.060114
## iter 180 value 420.954865
## iter 190 value 420.890612
## iter 200 value 420.781136
## final value 420.781136
## stopped after 200 iterations
nnPred<-predict(nn,TestSet,type = "class")
confusionMatrix(nnPred,TestClass)
## Confusion Matrix and Statistics
##
## Reference
## Prediction DOWN UP
## DOWN 184 91
## UP 42 160
##
## Accuracy : 0.7212
## 95% CI : (0.6786, 0.761)
## No Information Rate : 0.5262
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4468
## Mcnemar's Test P-Value : 3.153e-05
##
## Sensitivity : 0.8142
## Specificity : 0.6375
## Pos Pred Value : 0.6691
## Neg Pred Value : 0.7921
## Prevalence : 0.4738
## Detection Rate : 0.3857
## Detection Prevalence : 0.5765
## Balanced Accuracy : 0.7258
##
## 'Positive' Class : DOWN
##