Random Forest
Yashvi
6 September 2017
Problem Defination To find whether the email is SPam or Not Spam
The steps to predict using Random Forest
* Step 1: Consider 2/3rd data set as Training Data set
* Step 2: Make Multipile Decision Trees after training the dataset by default the number is 500
* Step 3: Each tree we do Decision Tree classification we get output
* Step 4: One record passess through all trees
** Title: SPAM E-mail Database **
Sources: (a) Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304 (b) Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835 (c) Generated: June-July 1999
Past Usage: (a) Hewlett-Packard Internal-only Technical Report. External forthcoming. (b) Determine whether a given email is spam or not. (c) ~7% misclassification error. False positives (marking good mail as spam) are very undesirable. If we insist on zero false positives in the training/testing set, 20-25% of the spam passed through the filter.
Relevant Information: The “spam” concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography… Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word ‘george’ and the area code ‘650’ are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.
For background on spam:
Cranor, Lorrie F., LaMacchia, Brian A. Spam!
Communications of the ACM, 41(8):74-83, 1998.Number of Instances: 4601 (1813 Spam = 39.4%)
Number of Attributes: 58 (57 continuous, 1 nominal class label)
Attribute Information: The last column of ‘spambase.data’ denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.
Most of the attributes indicate whether a particular word or character was frequently occuring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters. For the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes:
48 continuous real [0,100] attributes of type word_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail. A “word” in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.
6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) / total characters in e-mail
1 continuous real [1,…] attribute of type capital_run_length_average = average length of uninterrupted sequences of capital letters
1 continuous integer [1,…] attribute of type capital_run_length_longest = length of longest uninterrupted sequence of capital letters
1 continuous integer [1,…] attribute of type capital_run_length_total = sum of length of uninterrupted sequences of capital letters = total number of capital letters in the e-mail
1 nominal {0,1} class attribute of type spam = denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.
Missing Attribute Values: None
Class Distribution: Spam 1813 (39.4%) Non-Spam 2788 (60.6%)
Load Libs
library(plyr)## Warning: package 'plyr' was built under R version 3.3.3library(tidyr)## Warning: package 'tidyr' was built under R version 3.3.3library(dplyr)## Warning: package 'dplyr' was built under R version 3.3.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 3.3.3#install.packages("caret")
library(caret)## Warning: package 'caret' was built under R version 3.3.3## Loading required package: lattice#install.packages("randomForest")
library(randomForest)## Warning: package 'randomForest' was built under R version 3.3.3## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combine#install.packages("gbm")
library(gbm)## Warning: package 'gbm' was built under R version 3.3.3## Loading required package: survival##
## Attaching package: 'survival'## The following object is masked from 'package:caret':
##
## cluster## Loading required package: splines## Loading required package: parallel## Loaded gbm 2.1.3#install.packages("corrgram")
library(corrgram)## Warning: package 'corrgram' was built under R version 3.3.3##
## Attaching package: 'corrgram'## The following object is masked from 'package:plyr':
##
## baseballFunctions
detectNA <- function(inp) {
sum(is.na(inp))
}
detectCor <- function(x) {
cor(as.numeric(dfrDataset[, x]),
as.numeric(dfrDataset$status),
method="spearman")
}Load Dataset
dfrDataset <- read.csv("./spambase.csv", header=T, stringsAsFactors=T)
head(dfrDataset)## word_freq_make word_freq_address word_freq_all word_freq_3d
## 1 0.00 0.64 0.64 0
## 2 0.21 0.28 0.50 0
## 3 0.06 0.00 0.71 0
## 4 0.00 0.00 0.00 0
## 5 0.00 0.00 0.00 0
## 6 0.00 0.00 0.00 0
## word_freq_our word_freq_over word_freq_remove word_freq_internet
## 1 0.32 0.00 0.00 0.00
## 2 0.14 0.28 0.21 0.07
## 3 1.23 0.19 0.19 0.12
## 4 0.63 0.00 0.31 0.63
## 5 0.63 0.00 0.31 0.63
## 6 1.85 0.00 0.00 1.85
## word_freq_order word_freq_mail word_freq_receive word_freq_will
## 1 0.00 0.00 0.00 0.64
## 2 0.00 0.94 0.21 0.79
## 3 0.64 0.25 0.38 0.45
## 4 0.31 0.63 0.31 0.31
## 5 0.31 0.63 0.31 0.31
## 6 0.00 0.00 0.00 0.00
## word_freq_people word_freq_report word_freq_addresses word_freq_free
## 1 0.00 0.00 0.00 0.32
## 2 0.65 0.21 0.14 0.14
## 3 0.12 0.00 1.75 0.06
## 4 0.31 0.00 0.00 0.31
## 5 0.31 0.00 0.00 0.31
## 6 0.00 0.00 0.00 0.00
## word_freq_business word_freq_email word_freq_you word_freq_credit
## 1 0.00 1.29 1.93 0.00
## 2 0.07 0.28 3.47 0.00
## 3 0.06 1.03 1.36 0.32
## 4 0.00 0.00 3.18 0.00
## 5 0.00 0.00 3.18 0.00
## 6 0.00 0.00 0.00 0.00
## word_freq_your word_freq_font word_freq_000 word_freq_money word_freq_hp
## 1 0.96 0 0.00 0.00 0
## 2 1.59 0 0.43 0.43 0
## 3 0.51 0 1.16 0.06 0
## 4 0.31 0 0.00 0.00 0
## 5 0.31 0 0.00 0.00 0
## 6 0.00 0 0.00 0.00 0
## word_freq_hpl word_freq_george word_freq_650 word_freq_lab
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## word_freq_labs word_freq_telnet word_freq_857 word_freq_data
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## word_freq_415 word_freq_85 word_freq_technology word_freq_1999
## 1 0 0 0 0.00
## 2 0 0 0 0.07
## 3 0 0 0 0.00
## 4 0 0 0 0.00
## 5 0 0 0 0.00
## 6 0 0 0 0.00
## word_freq_parts word_freq_pm word_freq_direct word_freq_cs
## 1 0 0 0.00 0
## 2 0 0 0.00 0
## 3 0 0 0.06 0
## 4 0 0 0.00 0
## 5 0 0 0.00 0
## 6 0 0 0.00 0
## word_freq_meeting word_freq_original word_freq_project word_freq_re
## 1 0 0.00 0 0.00
## 2 0 0.00 0 0.00
## 3 0 0.12 0 0.06
## 4 0 0.00 0 0.00
## 5 0 0.00 0 0.00
## 6 0 0.00 0 0.00
## word_freq_edu word_freq_table word_freq_conference char_freq_.
## 1 0.00 0 0 0.00
## 2 0.00 0 0 0.00
## 3 0.06 0 0 0.01
## 4 0.00 0 0 0.00
## 5 0.00 0 0 0.00
## 6 0.00 0 0 0.00
## char_freq_..1 char_freq_..2 char_freq_..3 char_freq_..4 char_freq_..5
## 1 0.000 0 0.778 0.000 0.000
## 2 0.132 0 0.372 0.180 0.048
## 3 0.143 0 0.276 0.184 0.010
## 4 0.137 0 0.137 0.000 0.000
## 5 0.135 0 0.135 0.000 0.000
## 6 0.223 0 0.000 0.000 0.000
## capital_run_length_average capital_run_length_longest
## 1 3.756 61
## 2 5.114 101
## 3 9.821 485
## 4 3.537 40
## 5 3.537 40
## 6 3.000 15
## capital_run_length_total status
## 1 278 1
## 2 1028 1
## 3 2259 1
## 4 191 1
## 5 191 1
## 6 54 1Dataframe Stucture
str(dfrDataset)## 'data.frame': 4601 obs. of 58 variables:
## $ word_freq_make : num 0 0.21 0.06 0 0 0 0 0 0.15 0.06 ...
## $ word_freq_address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ...
## $ word_freq_all : num 0.64 0.5 0.71 0 0 0 0 0 0.46 0.77 ...
## $ word_freq_3d : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_our : num 0.32 0.14 1.23 0.63 0.63 1.85 1.92 1.88 0.61 0.19 ...
## $ word_freq_over : num 0 0.28 0.19 0 0 0 0 0 0 0.32 ...
## $ word_freq_remove : num 0 0.21 0.19 0.31 0.31 0 0 0 0.3 0.38 ...
## $ word_freq_internet : num 0 0.07 0.12 0.63 0.63 1.85 0 1.88 0 0 ...
## $ word_freq_order : num 0 0 0.64 0.31 0.31 0 0 0 0.92 0.06 ...
## $ word_freq_mail : num 0 0.94 0.25 0.63 0.63 0 0.64 0 0.76 0 ...
## $ word_freq_receive : num 0 0.21 0.38 0.31 0.31 0 0.96 0 0.76 0 ...
## $ word_freq_will : num 0.64 0.79 0.45 0.31 0.31 0 1.28 0 0.92 0.64 ...
## $ word_freq_people : num 0 0.65 0.12 0.31 0.31 0 0 0 0 0.25 ...
## $ word_freq_report : num 0 0.21 0 0 0 0 0 0 0 0 ...
## $ word_freq_addresses : num 0 0.14 1.75 0 0 0 0 0 0 0.12 ...
## $ word_freq_free : num 0.32 0.14 0.06 0.31 0.31 0 0.96 0 0 0 ...
## $ word_freq_business : num 0 0.07 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_email : num 1.29 0.28 1.03 0 0 0 0.32 0 0.15 0.12 ...
## $ word_freq_you : num 1.93 3.47 1.36 3.18 3.18 0 3.85 0 1.23 1.67 ...
## $ word_freq_credit : num 0 0 0.32 0 0 0 0 0 3.53 0.06 ...
## $ word_freq_your : num 0.96 1.59 0.51 0.31 0.31 0 0.64 0 2 0.71 ...
## $ word_freq_font : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_000 : num 0 0.43 1.16 0 0 0 0 0 0 0.19 ...
## $ word_freq_money : num 0 0.43 0.06 0 0 0 0 0 0.15 0 ...
## $ word_freq_hp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_hpl : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_george : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_650 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_lab : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_labs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_telnet : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_857 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_data : num 0 0 0 0 0 0 0 0 0.15 0 ...
## $ word_freq_415 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_85 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_technology : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_1999 : num 0 0.07 0 0 0 0 0 0 0 0 ...
## $ word_freq_parts : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_pm : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_direct : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_cs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_meeting : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_original : num 0 0 0.12 0 0 0 0 0 0.3 0 ...
## $ word_freq_project : num 0 0 0 0 0 0 0 0 0 0.06 ...
## $ word_freq_re : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_edu : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_table : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_conference : num 0 0 0 0 0 0 0 0 0 0 ...
## $ char_freq_. : num 0 0 0.01 0 0 0 0 0 0 0.04 ...
## $ char_freq_..1 : num 0 0.132 0.143 0.137 0.135 0.223 0.054 0.206 0.271 0.03 ...
## $ char_freq_..2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ char_freq_..3 : num 0.778 0.372 0.276 0.137 0.135 0 0.164 0 0.181 0.244 ...
## $ char_freq_..4 : num 0 0.18 0.184 0 0 0 0.054 0 0.203 0.081 ...
## $ char_freq_..5 : num 0 0.048 0.01 0 0 0 0 0 0.022 0 ...
## $ capital_run_length_average: num 3.76 5.11 9.82 3.54 3.54 ...
## $ capital_run_length_longest: int 61 101 485 40 40 15 4 11 445 43 ...
## $ capital_run_length_total : int 278 1028 2259 191 191 54 112 49 1257 749 ...
## $ status : int 1 1 1 1 1 1 1 1 1 1 ...Dataframe Summary
lapply(dfrDataset, FUN=summary)## $word_freq_make
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1046 0.0000 4.5400
##
## $word_freq_address
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.213 0.000 14.280
##
## $word_freq_all
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2807 0.4200 5.1000
##
## $word_freq_3d
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06542 0.00000 42.81000
##
## $word_freq_our
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3122 0.3800 10.0000
##
## $word_freq_over
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0959 0.0000 5.8800
##
## $word_freq_remove
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1142 0.0000 7.2700
##
## $word_freq_internet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1053 0.0000 11.1100
##
## $word_freq_order
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09007 0.00000 5.26000
##
## $word_freq_mail
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2394 0.1600 18.1800
##
## $word_freq_receive
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05982 0.00000 2.61000
##
## $word_freq_will
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1000 0.5417 0.8000 9.6700
##
## $word_freq_people
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09393 0.00000 5.55000
##
## $word_freq_report
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05863 0.00000 10.00000
##
## $word_freq_addresses
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0492 0.0000 4.4100
##
## $word_freq_free
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2488 0.1000 20.0000
##
## $word_freq_business
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1426 0.0000 7.1400
##
## $word_freq_email
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1847 0.0000 9.0900
##
## $word_freq_you
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.310 1.662 2.640 18.750
##
## $word_freq_credit
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08558 0.00000 18.18000
##
## $word_freq_your
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2200 0.8098 1.2700 11.1100
##
## $word_freq_font
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1212 0.0000 17.1000
##
## $word_freq_000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1016 0.0000 5.4500
##
## $word_freq_money
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09427 0.00000 12.50000
##
## $word_freq_hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5495 0.0000 20.8300
##
## $word_freq_hpl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2654 0.0000 16.6600
##
## $word_freq_george
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7673 0.0000 33.3300
##
## $word_freq_650
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1248 0.0000 9.0900
##
## $word_freq_lab
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09892 0.00000 14.28000
##
## $word_freq_labs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1029 0.0000 5.8800
##
## $word_freq_telnet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06475 0.00000 12.50000
##
## $word_freq_857
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04705 0.00000 4.76000
##
## $word_freq_data
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09723 0.00000 18.18000
##
## $word_freq_415
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04784 0.00000 4.76000
##
## $word_freq_85
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1054 0.0000 20.0000
##
## $word_freq_technology
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09748 0.00000 7.69000
##
## $word_freq_1999
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.137 0.000 6.890
##
## $word_freq_parts
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0132 0.0000 8.3300
##
## $word_freq_pm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07863 0.00000 11.11000
##
## $word_freq_direct
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06483 0.00000 4.76000
##
## $word_freq_cs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04367 0.00000 7.14000
##
## $word_freq_meeting
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1323 0.0000 14.2800
##
## $word_freq_original
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0461 0.0000 3.5700
##
## $word_freq_project
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0792 0.0000 20.0000
##
## $word_freq_re
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3012 0.1100 21.4200
##
## $word_freq_edu
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1798 0.0000 22.0500
##
## $word_freq_table
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.005444 0.000000 2.170000
##
## $word_freq_conference
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03187 0.00000 10.00000
##
## $char_freq_.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03857 0.00000 4.38500
##
## $char_freq_..1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.065 0.139 0.188 9.752
##
## $char_freq_..2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01698 0.00000 4.08100
##
## $char_freq_..3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2691 0.3150 32.4800
##
## $char_freq_..4
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07581 0.05200 6.00300
##
## $char_freq_..5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04424 0.00000 19.83000
##
## $capital_run_length_average
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.588 2.276 5.192 3.706 1102.000
##
## $capital_run_length_longest
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.00 15.00 52.17 43.00 9989.00
##
## $capital_run_length_total
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 35.0 95.0 283.3 266.0 15840.0
##
## $status
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.394 1.000 1.000Missing Data
lapply(dfrDataset, FUN=detectNA)## $word_freq_make
## [1] 0
##
## $word_freq_address
## [1] 0
##
## $word_freq_all
## [1] 0
##
## $word_freq_3d
## [1] 0
##
## $word_freq_our
## [1] 0
##
## $word_freq_over
## [1] 0
##
## $word_freq_remove
## [1] 0
##
## $word_freq_internet
## [1] 0
##
## $word_freq_order
## [1] 0
##
## $word_freq_mail
## [1] 0
##
## $word_freq_receive
## [1] 0
##
## $word_freq_will
## [1] 0
##
## $word_freq_people
## [1] 0
##
## $word_freq_report
## [1] 0
##
## $word_freq_addresses
## [1] 0
##
## $word_freq_free
## [1] 0
##
## $word_freq_business
## [1] 0
##
## $word_freq_email
## [1] 0
##
## $word_freq_you
## [1] 0
##
## $word_freq_credit
## [1] 0
##
## $word_freq_your
## [1] 0
##
## $word_freq_font
## [1] 0
##
## $word_freq_000
## [1] 0
##
## $word_freq_money
## [1] 0
##
## $word_freq_hp
## [1] 0
##
## $word_freq_hpl
## [1] 0
##
## $word_freq_george
## [1] 0
##
## $word_freq_650
## [1] 0
##
## $word_freq_lab
## [1] 0
##
## $word_freq_labs
## [1] 0
##
## $word_freq_telnet
## [1] 0
##
## $word_freq_857
## [1] 0
##
## $word_freq_data
## [1] 0
##
## $word_freq_415
## [1] 0
##
## $word_freq_85
## [1] 0
##
## $word_freq_technology
## [1] 0
##
## $word_freq_1999
## [1] 0
##
## $word_freq_parts
## [1] 0
##
## $word_freq_pm
## [1] 0
##
## $word_freq_direct
## [1] 0
##
## $word_freq_cs
## [1] 0
##
## $word_freq_meeting
## [1] 0
##
## $word_freq_original
## [1] 0
##
## $word_freq_project
## [1] 0
##
## $word_freq_re
## [1] 0
##
## $word_freq_edu
## [1] 0
##
## $word_freq_table
## [1] 0
##
## $word_freq_conference
## [1] 0
##
## $char_freq_.
## [1] 0
##
## $char_freq_..1
## [1] 0
##
## $char_freq_..2
## [1] 0
##
## $char_freq_..3
## [1] 0
##
## $char_freq_..4
## [1] 0
##
## $char_freq_..5
## [1] 0
##
## $capital_run_length_average
## [1] 0
##
## $capital_run_length_longest
## [1] 0
##
## $capital_run_length_total
## [1] 0
##
## $status
## [1] 0Finding the missing data in the dataset ** If there are missing values we use Random Forest KNN method We can also use Proximity for finding missing values and outliers Check output**
dfrstatus <- summarise(group_by(dfrDataset, status), count=n())
ggplot(dfrstatus, aes(x=status, y=count)) +
geom_bar(stat="identity", aes(fill=count)) +
labs(title="status Frequency Distribution") +
labs(x="status") +
labs(y="Counts")
Here we take output variable as Status as it has categorical values and Random forest works better on Categorical variables then on binary variables
Find Corelations
## find correlations
vcnCorsData <- abs(sapply(colnames(dfrDataset), detectCor))
summary(vcnCorsData)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.002525 0.148800 0.253300 0.276900 0.354700 1.000000Show Corelations
vcnCorsData## word_freq_make word_freq_address
## 0.24069974 0.29750940
## word_freq_all word_freq_3d
## 0.33283147 0.09077776
## word_freq_our word_freq_over
## 0.40913946 0.31864550
## word_freq_remove word_freq_internet
## 0.51877779 0.34379623
## word_freq_order word_freq_mail
## 0.30073703 0.29682394
## word_freq_receive word_freq_will
## 0.35496682 0.14847653
## word_freq_people word_freq_report
## 0.21287588 0.14977533
## word_freq_addresses word_freq_free
## 0.26515743 0.50416922
## word_freq_business word_freq_email
## 0.35290749 0.29909391
## word_freq_you word_freq_credit
## 0.36110406 0.32418657
## word_freq_your word_freq_font
## 0.50159062 0.13797471
## word_freq_000 word_freq_money
## 0.42580256 0.47215455
## word_freq_hp word_freq_hpl
## 0.39981558 0.34188069
## word_freq_george word_freq_650
## 0.35393063 0.22619064
## word_freq_lab word_freq_labs
## 0.22068802 0.24580530
## word_freq_telnet word_freq_857
## 0.20467400 0.16983798
## word_freq_data word_freq_415
## 0.15756347 0.15802818
## word_freq_85 word_freq_technology
## 0.21413087 0.16680254
## word_freq_1999 word_freq_parts
## 0.26070752 0.00252536
## word_freq_pm word_freq_direct
## 0.14721389 0.02813193
## word_freq_cs word_freq_meeting
## 0.14453750 0.19574176
## word_freq_original word_freq_project
## 0.10781412 0.14453744
## word_freq_re word_freq_edu
## 0.07176763 0.19702549
## word_freq_table word_freq_conference
## 0.02266674 0.13903044
## char_freq_. char_freq_..1
## 0.05683530 0.03263555
## char_freq_..2 char_freq_..3
## 0.11122690 0.59785363
## char_freq_..4 char_freq_..5
## 0.56563314 0.26668614
## capital_run_length_average capital_run_length_longest
## 0.48794983 0.51515693
## capital_run_length_total status
## 0.44397367 1.00000000checking correlations of the output variables with the other variables Plot Corelations
corrgram(dfrDataset)
High Corelations
vcnCorsData[vcnCorsData>0.8]## status
## 1Creating a vector where the correlations are greater then 0.8 ,as the above data has only one variable which has greater then 0.8 hence the status is 1
Create Column Result
dfrDataset <- mutate(dfrDataset, Result= ifelse(dfrDataset$status < 1,'Not Spam','spam'))
dfrDataset$Result <- as.factor(dfrDataset$Result)
table(dfrDataset$Result)##
## Not Spam spam
## 2788 1813Column result gives the count of the variables which are spam and not spam , values which are less then 1 are considered as not spam, the balance are considered as spam
Dataset Split
set.seed(707)
vctTrnRecs <- createDataPartition(y=dfrDataset$status, p=0.7, list=FALSE)
dfrTrnData <- dfrDataset[vctTrnRecs,]
dfrTstData <- dfrDataset[-vctTrnRecs,]Spilting the dataset into Training and Test Dataset ** 70% of the dataset is spilt as train data and 30 % as test data Training Dataset RowCount & ColCount**
dim(dfrTrnData)## [1] 3221 59** out of the total 4601 variables ,3221 variables are in the Training Dataset **
Training Dataset Head
head(dfrTrnData)## word_freq_make word_freq_address word_freq_all word_freq_3d
## 2 0.21 0.28 0.50 0
## 3 0.06 0.00 0.71 0
## 4 0.00 0.00 0.00 0
## 10 0.06 0.12 0.77 0
## 11 0.00 0.00 0.00 0
## 12 0.00 0.00 0.25 0
## word_freq_our word_freq_over word_freq_remove word_freq_internet
## 2 0.14 0.28 0.21 0.07
## 3 1.23 0.19 0.19 0.12
## 4 0.63 0.00 0.31 0.63
## 10 0.19 0.32 0.38 0.00
## 11 0.00 0.00 0.96 0.00
## 12 0.38 0.25 0.25 0.00
## word_freq_order word_freq_mail word_freq_receive word_freq_will
## 2 0.00 0.94 0.21 0.79
## 3 0.64 0.25 0.38 0.45
## 4 0.31 0.63 0.31 0.31
## 10 0.06 0.00 0.00 0.64
## 11 0.00 1.92 0.96 0.00
## 12 0.00 0.00 0.12 0.12
## word_freq_people word_freq_report word_freq_addresses word_freq_free
## 2 0.65 0.21 0.14 0.14
## 3 0.12 0.00 1.75 0.06
## 4 0.31 0.00 0.00 0.31
## 10 0.25 0.00 0.12 0.00
## 11 0.00 0.00 0.00 0.00
## 12 0.12 0.00 0.00 0.00
## word_freq_business word_freq_email word_freq_you word_freq_credit
## 2 0.07 0.28 3.47 0.00
## 3 0.06 1.03 1.36 0.32
## 4 0.00 0.00 3.18 0.00
## 10 0.00 0.12 1.67 0.06
## 11 0.00 0.96 3.84 0.00
## 12 0.00 0.00 1.16 0.00
## word_freq_your word_freq_font word_freq_000 word_freq_money
## 2 1.59 0 0.43 0.43
## 3 0.51 0 1.16 0.06
## 4 0.31 0 0.00 0.00
## 10 0.71 0 0.19 0.00
## 11 0.96 0 0.00 0.00
## 12 0.77 0 0.00 0.00
## word_freq_hp word_freq_hpl word_freq_george word_freq_650 word_freq_lab
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 0 0 0 0 0
## word_freq_labs word_freq_telnet word_freq_857 word_freq_data
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## word_freq_415 word_freq_85 word_freq_technology word_freq_1999
## 2 0 0 0 0.07
## 3 0 0 0 0.00
## 4 0 0 0 0.00
## 10 0 0 0 0.00
## 11 0 0 0 0.00
## 12 0 0 0 0.00
## word_freq_parts word_freq_pm word_freq_direct word_freq_cs
## 2 0 0 0.00 0
## 3 0 0 0.06 0
## 4 0 0 0.00 0
## 10 0 0 0.00 0
## 11 0 0 0.96 0
## 12 0 0 0.00 0
## word_freq_meeting word_freq_original word_freq_project word_freq_re
## 2 0 0.00 0.00 0.00
## 3 0 0.12 0.00 0.06
## 4 0 0.00 0.00 0.00
## 10 0 0.00 0.06 0.00
## 11 0 0.00 0.00 0.00
## 12 0 0.00 0.00 0.00
## word_freq_edu word_freq_table word_freq_conference char_freq_.
## 2 0.00 0 0 0.000
## 3 0.06 0 0 0.010
## 4 0.00 0 0 0.000
## 10 0.00 0 0 0.040
## 11 0.00 0 0 0.000
## 12 0.00 0 0 0.022
## char_freq_..1 char_freq_..2 char_freq_..3 char_freq_..4 char_freq_..5
## 2 0.132 0 0.372 0.180 0.048
## 3 0.143 0 0.276 0.184 0.010
## 4 0.137 0 0.137 0.000 0.000
## 10 0.030 0 0.244 0.081 0.000
## 11 0.000 0 0.462 0.000 0.000
## 12 0.044 0 0.663 0.000 0.000
## capital_run_length_average capital_run_length_longest
## 2 5.114 101
## 3 9.821 485
## 4 3.537 40
## 10 1.729 43
## 11 1.312 6
## 12 1.243 11
## capital_run_length_total status Result
## 2 1028 1 spam
## 3 2259 1 spam
## 4 191 1 spam
## 10 749 1 spam
## 11 21 1 spam
## 12 184 1 spamTesting Dataset Head
head(dfrTstData)## word_freq_make word_freq_address word_freq_all word_freq_3d
## 1 0.00 0.64 0.64 0
## 5 0.00 0.00 0.00 0
## 6 0.00 0.00 0.00 0
## 7 0.00 0.00 0.00 0
## 8 0.00 0.00 0.00 0
## 9 0.15 0.00 0.46 0
## word_freq_our word_freq_over word_freq_remove word_freq_internet
## 1 0.32 0 0.00 0.00
## 5 0.63 0 0.31 0.63
## 6 1.85 0 0.00 1.85
## 7 1.92 0 0.00 0.00
## 8 1.88 0 0.00 1.88
## 9 0.61 0 0.30 0.00
## word_freq_order word_freq_mail word_freq_receive word_freq_will
## 1 0.00 0.00 0.00 0.64
## 5 0.31 0.63 0.31 0.31
## 6 0.00 0.00 0.00 0.00
## 7 0.00 0.64 0.96 1.28
## 8 0.00 0.00 0.00 0.00
## 9 0.92 0.76 0.76 0.92
## word_freq_people word_freq_report word_freq_addresses word_freq_free
## 1 0.00 0 0 0.32
## 5 0.31 0 0 0.31
## 6 0.00 0 0 0.00
## 7 0.00 0 0 0.96
## 8 0.00 0 0 0.00
## 9 0.00 0 0 0.00
## word_freq_business word_freq_email word_freq_you word_freq_credit
## 1 0 1.29 1.93 0.00
## 5 0 0.00 3.18 0.00
## 6 0 0.00 0.00 0.00
## 7 0 0.32 3.85 0.00
## 8 0 0.00 0.00 0.00
## 9 0 0.15 1.23 3.53
## word_freq_your word_freq_font word_freq_000 word_freq_money word_freq_hp
## 1 0.96 0 0 0.00 0
## 5 0.31 0 0 0.00 0
## 6 0.00 0 0 0.00 0
## 7 0.64 0 0 0.00 0
## 8 0.00 0 0 0.00 0
## 9 2.00 0 0 0.15 0
## word_freq_hpl word_freq_george word_freq_650 word_freq_lab
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## word_freq_labs word_freq_telnet word_freq_857 word_freq_data
## 1 0 0 0 0.00
## 5 0 0 0 0.00
## 6 0 0 0 0.00
## 7 0 0 0 0.00
## 8 0 0 0 0.00
## 9 0 0 0 0.15
## word_freq_415 word_freq_85 word_freq_technology word_freq_1999
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## word_freq_parts word_freq_pm word_freq_direct word_freq_cs
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## word_freq_meeting word_freq_original word_freq_project word_freq_re
## 1 0 0.0 0 0
## 5 0 0.0 0 0
## 6 0 0.0 0 0
## 7 0 0.0 0 0
## 8 0 0.0 0 0
## 9 0 0.3 0 0
## word_freq_edu word_freq_table word_freq_conference char_freq_.
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## char_freq_..1 char_freq_..2 char_freq_..3 char_freq_..4 char_freq_..5
## 1 0.000 0 0.778 0.000 0.000
## 5 0.135 0 0.135 0.000 0.000
## 6 0.223 0 0.000 0.000 0.000
## 7 0.054 0 0.164 0.054 0.000
## 8 0.206 0 0.000 0.000 0.000
## 9 0.271 0 0.181 0.203 0.022
## capital_run_length_average capital_run_length_longest
## 1 3.756 61
## 5 3.537 40
## 6 3.000 15
## 7 1.671 4
## 8 2.450 11
## 9 9.744 445
## capital_run_length_total status Result
## 1 278 1 spam
## 5 191 1 spam
## 6 54 1 spam
## 7 112 1 spam
## 8 49 1 spam
## 9 1257 1 spamTraining Dataset Summary
lapply(dfrTrnData, FUN=summary)## $word_freq_make
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1001 0.0000 4.5400
##
## $word_freq_address
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1901 0.0000 14.2800
##
## $word_freq_all
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.279 0.400 5.100
##
## $word_freq_3d
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05219 0.00000 42.81000
##
## $word_freq_our
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3027 0.3600 10.0000
##
## $word_freq_over
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09372 0.00000 2.63000
##
## $word_freq_remove
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1146 0.0000 7.2700
##
## $word_freq_internet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.112 0.000 11.110
##
## $word_freq_order
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08978 0.00000 5.26000
##
## $word_freq_mail
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2288 0.1300 18.1800
##
## $word_freq_receive
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05888 0.00000 2.61000
##
## $word_freq_will
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1000 0.5349 0.7700 7.6900
##
## $word_freq_people
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09223 0.00000 5.55000
##
## $word_freq_report
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05904 0.00000 10.00000
##
## $word_freq_addresses
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04824 0.00000 4.41000
##
## $word_freq_free
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2464 0.1000 16.6600
##
## $word_freq_business
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.138 0.000 7.140
##
## $word_freq_email
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1818 0.0000 9.0900
##
## $word_freq_you
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.290 1.671 2.660 18.750
##
## $word_freq_credit
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08912 0.00000 18.18000
##
## $word_freq_your
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2000 0.7921 1.2700 11.1100
##
## $word_freq_font
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1223 0.0000 15.4300
##
## $word_freq_000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1051 0.0000 5.4500
##
## $word_freq_money
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09648 0.00000 12.50000
##
## $word_freq_hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5416 0.0000 20.8300
##
## $word_freq_hpl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2673 0.0000 10.8600
##
## $word_freq_george
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7712 0.0000 33.3300
##
## $word_freq_650
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1322 0.0000 9.0900
##
## $word_freq_lab
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09695 0.00000 14.28000
##
## $word_freq_labs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1046 0.0000 5.8800
##
## $word_freq_telnet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06896 0.00000 12.50000
##
## $word_freq_857
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04833 0.00000 4.76000
##
## $word_freq_data
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1035 0.0000 18.1800
##
## $word_freq_415
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04945 0.00000 4.76000
##
## $word_freq_85
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1092 0.0000 20.0000
##
## $word_freq_technology
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1037 0.0000 7.6900
##
## $word_freq_1999
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1381 0.0000 5.0500
##
## $word_freq_parts
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01352 0.00000 8.33000
##
## $word_freq_pm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07996 0.00000 9.75000
##
## $word_freq_direct
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06304 0.00000 4.76000
##
## $word_freq_cs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04513 0.00000 7.14000
##
## $word_freq_meeting
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1367 0.0000 14.2800
##
## $word_freq_original
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04738 0.00000 3.57000
##
## $word_freq_project
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08829 0.00000 20.00000
##
## $word_freq_re
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2993 0.1200 20.0000
##
## $word_freq_edu
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1828 0.0000 22.0500
##
## $word_freq_table
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.004713 0.000000 1.910000
##
## $word_freq_conference
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03038 0.00000 10.00000
##
## $char_freq_.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04054 0.00000 4.38500
##
## $char_freq_..1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0660 0.1399 0.1890 5.2770
##
## $char_freq_..2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01506 0.00000 4.08100
##
## $char_freq_..3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2741 0.3110 32.4800
##
## $char_freq_..4
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07403 0.04800 5.30000
##
## $char_freq_..5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03887 0.00000 13.13000
##
## $capital_run_length_average
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.564 2.250 5.280 3.706 1102.000
##
## $capital_run_length_longest
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.00 14.00 49.71 43.00 2204.00
##
## $capital_run_length_total
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 35 94 282 268 15840
##
## $status
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3971 1.0000 1.0000
##
## $Result
## Not Spam spam
## 1942 1279Testing Dataset Summary
lapply(dfrTstData, FUN=summary)## $word_freq_make
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.115 0.000 4.000
##
## $word_freq_address
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2666 0.0000 14.2800
##
## $word_freq_all
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2844 0.4500 4.5400
##
## $word_freq_3d
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09632 0.00000 40.13000
##
## $word_freq_our
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3346 0.4325 7.1400
##
## $word_freq_over
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.101 0.000 5.880
##
## $word_freq_remove
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1133 0.0000 7.2700
##
## $word_freq_internet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08964 0.00000 4.62000
##
## $word_freq_order
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09074 0.00000 3.33000
##
## $word_freq_mail
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2642 0.2200 11.1100
##
## $word_freq_receive
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06204 0.00000 2.00000
##
## $word_freq_will
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1200 0.5576 0.8500 9.6700
##
## $word_freq_people
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09789 0.00000 5.55000
##
## $word_freq_report
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05767 0.00000 5.55000
##
## $word_freq_addresses
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05145 0.00000 2.31000
##
## $word_freq_free
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2546 0.1125 20.0000
##
## $word_freq_business
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1532 0.0000 4.8700
##
## $word_freq_email
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1917 0.0000 4.5100
##
## $word_freq_you
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.330 1.641 2.590 14.280
##
## $word_freq_credit
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07731 0.00000 6.25000
##
## $word_freq_your
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2700 0.8509 1.2800 10.7100
##
## $word_freq_font
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1186 0.0000 17.1000
##
## $word_freq_000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0937 0.0000 3.3800
##
## $word_freq_money
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0891 0.0000 5.9800
##
## $word_freq_hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5679 0.0000 20.0000
##
## $word_freq_hpl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2609 0.0000 16.6600
##
## $word_freq_george
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7582 0.0000 33.3300
##
## $word_freq_650
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1078 0.0000 4.7600
##
## $word_freq_lab
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1035 0.0000 10.0000
##
## $word_freq_labs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09869 0.00000 4.76000
##
## $word_freq_telnet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05493 0.00000 4.76000
##
## $word_freq_857
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04405 0.00000 4.76000
##
## $word_freq_data
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08264 0.00000 8.33000
##
## $word_freq_415
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04407 0.00000 4.76000
##
## $word_freq_85
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09651 0.00000 4.76000
##
## $word_freq_technology
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08303 0.00000 4.76000
##
## $word_freq_1999
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1344 0.0000 6.8900
##
## $word_freq_parts
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01246 0.00000 7.40000
##
## $word_freq_pm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07551 0.00000 11.11000
##
## $word_freq_direct
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06901 0.00000 4.76000
##
## $word_freq_cs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04025 0.00000 4.75000
##
## $word_freq_meeting
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1221 0.0000 7.6900
##
## $word_freq_original
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04311 0.00000 3.44000
##
## $word_freq_project
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05797 0.00000 4.54000
##
## $word_freq_re
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3056 0.0800 21.4200
##
## $word_freq_edu
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1729 0.0000 9.5200
##
## $word_freq_table
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.007152 0.000000 2.170000
##
## $word_freq_conference
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03534 0.00000 8.33000
##
## $char_freq_.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03398 0.00000 4.18700
##
## $char_freq_..1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0600 0.1371 0.1772 9.7520
##
## $char_freq_..2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.02145 0.00000 2.77700
##
## $char_freq_..3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2573 0.3302 5.8440
##
## $char_freq_..4
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07996 0.06225 6.00300
##
## $char_freq_..5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05676 0.00000 19.83000
##
## $capital_run_length_average
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.630 2.318 4.984 3.706 443.700
##
## $capital_run_length_longest
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.00 15.00 57.92 45.00 9989.00
##
## $capital_run_length_total
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 35.0 95.5 286.3 259.2 10060.0
##
## $status
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.387 1.000 1.000
##
## $Result
## Not Spam spam
## 846 534Create Model - Random Forest (Default)
## set seed
set.seed(707)
# mtry
myMtry=sqrt(ncol(dfrTrnData)-2)
myNtrees=500
# start time
vctProcStrt <- proc.time()
# random forest (default)
mdlRndForDef <- randomForest(Result~.-status, data=dfrTrnData,
mtry=myMtry, ntree=myNtrees)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 25.92"** Here we create Random forest based on the random forest (default) method In this method random observations are used to grow trees ,Random variables are used for spilting each node View Model - Default Random Forest**
mdlRndForDef##
## Call:
## randomForest(formula = Result ~ . - status, data = dfrTrnData, mtry = myMtry, ntree = myNtrees)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 8
##
## OOB estimate of error rate: 4.75%
## Confusion matrix:
## Not Spam spam class.error
## Not Spam 1887 55 0.02832132
## spam 98 1181 0.07662236View Model Summary - Default Random Forest
summary(mdlRndForDef)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 3221 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 6442 matrix numeric
## oob.times 3221 -none- numeric
## classes 2 -none- character
## importance 57 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3221 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callPrediction - Test Data - Random Forest (Default)
vctRndForDef <- predict(mdlRndForDef, newdata=dfrTstData)
cmxRndForDef <- confusionMatrix(vctRndForDef, dfrTstData$Result)
cmxRndForDef## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam spam
## Not Spam 808 28
## spam 38 506
##
## Accuracy : 0.9522
## 95% CI : (0.9396, 0.9628)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8995
## Mcnemar's Test P-Value : 0.2679
##
## Sensitivity : 0.9551
## Specificity : 0.9476
## Pos Pred Value : 0.9665
## Neg Pred Value : 0.9301
## Prevalence : 0.6130
## Detection Rate : 0.5855
## Detection Prevalence : 0.6058
## Balanced Accuracy : 0.9513
##
## 'Positive' Class : Not Spam
## ** After the model is created we use the test dataset for prediction purposeand here we get a accuracy of 95%**
Create Model - Random Forest (RFM)
## set seed
set.seed(707)
# start time
vctProcStrt <- proc.time()
# random forest (default)
myControl <- trainControl(method="cv", number=10, repeats=3)
myMetric <- "Accuracy"
myMtry <- sqrt(ncol(dfrTrnData)-2)
#myNtrees <- 500
myTuneGrid <- expand.grid(.mtry=myMtry)
mdlRndForRfm <- train(Result~.-status, data=dfrTrnData, method="rf",
verbose=F,metric=myMetric, trControl=myControl,
tuneGrid=myTuneGrid)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 244.19"** Create a Random forest using the CV method **
View Model - Random Forest (RFM)
mdlRndForRfm## Random Forest
##
## 3221 samples
## 58 predictor
## 2 classes: 'Not Spam', 'spam'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 2898, 2899, 2900, 2898, 2899, 2899, ...
## Resampling results:
##
## Accuracy Kappa
## 0.9537468 0.9028854
##
## Tuning parameter 'mtry' was held constant at a value of 7.549834View Model Summary - Random Forest (RFM)
summary(mdlRndForRfm)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 3221 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 6442 matrix numeric
## oob.times 3221 -none- numeric
## classes 2 -none- character
## importance 57 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3221 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## param 1 -none- listPrediction - Test Data - Random Forest (RFM)
vctRndForRfm <- predict(mdlRndForRfm, newdata=dfrTstData)
cmxRndForRfm <- confusionMatrix(vctRndForRfm, dfrTstData$Result)
cmxRndForRfm## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam spam
## Not Spam 807 30
## spam 39 504
##
## Accuracy : 0.95
## 95% CI : (0.9371, 0.9609)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8949
## Mcnemar's Test P-Value : 0.3355
##
## Sensitivity : 0.9539
## Specificity : 0.9438
## Pos Pred Value : 0.9642
## Neg Pred Value : 0.9282
## Prevalence : 0.6130
## Detection Rate : 0.5848
## Detection Prevalence : 0.6065
## Balanced Accuracy : 0.9489
##
## 'Positive' Class : Not Spam
## ** By using the CV method we get an accuracy of 95 % **
Create Model - Random Forest (GBM)
## set seed
set.seed(707)
# start time
vctProcStrt <- proc.time()
# random forest (default)
myControl <- trainControl(method="repeatedcv", number=10, repeats=3)
myMetric <- "Accuracy"
myMtry <- sqrt(ncol(dfrTrnData)-2)
#myNtrees <- 500
myTuneGrid <- expand.grid(.mtry=myMtry)
mdlRndForGbm <- train(Result~.-status, data=dfrTrnData, method="gbm",
verbose=F, metric=myMetric, trControl=myControl)
# tuneGrid=myTuneGrid, ntree=myNtrees)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 263.83"** Creating a Random forest model using repeatedcv ,where same records are used in different datasets It gives Out of the Bag Value and Out of the Bag Error **
View Model - Random Forest (GBM)
mdlRndForGbm## Stochastic Gradient Boosting
##
## 3221 samples
## 58 predictor
## 2 classes: 'Not Spam', 'spam'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 2898, 2899, 2900, 2898, 2899, 2899, ...
## Resampling results across tuning parameters:
##
## interaction.depth n.trees Accuracy Kappa
## 1 50 0.9119364 0.8120901
## 1 100 0.9305600 0.8530141
## 1 150 0.9349085 0.8625545
## 2 50 0.9303539 0.8527418
## 2 100 0.9398768 0.8733088
## 2 150 0.9437035 0.8814997
## 3 50 0.9347011 0.8622468
## 3 100 0.9437057 0.8814933
## 3 150 0.9485670 0.8919259
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 150,
## interaction.depth = 3, shrinkage = 0.1 and n.minobsinnode = 10.View Model Summary - Random Forest (GBM)
summary(mdlRndForGbm)
## var rel.inf
## char_freq_..3 char_freq_..3 25.13448479
## char_freq_..4 char_freq_..4 17.73569393
## word_freq_remove word_freq_remove 10.29166207
## word_freq_free word_freq_free 8.03319338
## word_freq_hp word_freq_hp 7.32712662
## capital_run_length_average capital_run_length_average 5.39872005
## word_freq_your word_freq_your 5.17722583
## capital_run_length_longest capital_run_length_longest 3.06771805
## capital_run_length_total capital_run_length_total 2.37637288
## word_freq_edu word_freq_edu 2.36886986
## word_freq_george word_freq_george 2.23737649
## word_freq_our word_freq_our 1.91405678
## word_freq_money word_freq_money 1.86852638
## word_freq_1999 word_freq_1999 1.19302329
## word_freq_000 word_freq_000 0.89483913
## word_freq_you word_freq_you 0.56772121
## word_freq_meeting word_freq_meeting 0.56184568
## word_freq_re word_freq_re 0.49338930
## word_freq_internet word_freq_internet 0.45674727
## char_freq_. char_freq_. 0.38814204
## word_freq_will word_freq_will 0.35496666
## word_freq_receive word_freq_receive 0.31982874
## word_freq_650 word_freq_650 0.30196695
## char_freq_..1 char_freq_..1 0.28366668
## word_freq_font word_freq_font 0.18729687
## word_freq_email word_freq_email 0.18116648
## word_freq_business word_freq_business 0.14833434
## word_freq_hpl word_freq_hpl 0.14274872
## word_freq_technology word_freq_technology 0.10444119
## word_freq_over word_freq_over 0.09742430
## word_freq_report word_freq_report 0.09059711
## word_freq_all word_freq_all 0.07754641
## word_freq_3d word_freq_3d 0.06375843
## word_freq_credit word_freq_credit 0.03340249
## word_freq_project word_freq_project 0.03264960
## word_freq_make word_freq_make 0.02808895
## word_freq_pm word_freq_pm 0.02398858
## word_freq_conference word_freq_conference 0.01519494
## word_freq_mail word_freq_mail 0.01326804
## word_freq_parts word_freq_parts 0.01292947
## word_freq_address word_freq_address 0.00000000
## word_freq_order word_freq_order 0.00000000
## word_freq_people word_freq_people 0.00000000
## word_freq_addresses word_freq_addresses 0.00000000
## word_freq_lab word_freq_lab 0.00000000
## word_freq_labs word_freq_labs 0.00000000
## word_freq_telnet word_freq_telnet 0.00000000
## word_freq_857 word_freq_857 0.00000000
## word_freq_data word_freq_data 0.00000000
## word_freq_415 word_freq_415 0.00000000
## word_freq_85 word_freq_85 0.00000000
## word_freq_direct word_freq_direct 0.00000000
## word_freq_cs word_freq_cs 0.00000000
## word_freq_original word_freq_original 0.00000000
## word_freq_table word_freq_table 0.00000000
## char_freq_..2 char_freq_..2 0.00000000
## char_freq_..5 char_freq_..5 0.00000000** The view model gives a graphical representation of the model using the repeatedcv method Prediction - Test Data - Random Forest (GBM)**
vctRndForGbm <- predict(mdlRndForGbm, newdata=dfrTstData)
cmxRndForGbm <- confusionMatrix(vctRndForGbm, dfrTstData$Result)
cmxRndForGbm## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam spam
## Not Spam 804 29
## spam 42 505
##
## Accuracy : 0.9486
## 95% CI : (0.9355, 0.9596)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.892
## Mcnemar's Test P-Value : 0.1544
##
## Sensitivity : 0.9504
## Specificity : 0.9457
## Pos Pred Value : 0.9652
## Neg Pred Value : 0.9232
## Prevalence : 0.6130
## Detection Rate : 0.5826
## Detection Prevalence : 0.6036
## Balanced Accuracy : 0.9480
##
## 'Positive' Class : Not Spam
## ** We predict the model using the repeatedcv method and get an accuracy of 94% **
Create Model - Random Forest (OOB)
## set seed
set.seed(707)
# start time
vctProcStrt <- proc.time()
# random forest (default)
myControl <- trainControl(method="oob", number=10, repeats=3)
myMetric <- "Accuracy"
myMtry <- 10 #sqrt(ncol(dfrTrnData)-2)
myNtrees <- 500
myTuneGrid <- expand.grid(.mtry=myMtry)
mdlRndForoob <- train(Result~.-status, data=dfrTrnData,
verbose=F, metric=myMetric, trControl=myControl,
tuneGrid=myTuneGrid, ntree=myNtrees)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 52.58"** We create a Random Forest model using the OOB method Not Classified/Missclassified **
View Model - Random Formbest (OOB)
mdlRndForoob## Random Forest
##
## 3221 samples
## 58 predictor
## 2 classes: 'Not Spam', 'spam'
##
## No pre-processing
## Resampling results:
##
## Accuracy Kappa
## 0.9509469 0.8971414
##
## Tuning parameter 'mtry' was held constant at a value of 10View Model Summary - Random Forest (OOB)
summary(mdlRndForoob)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 3221 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 6442 matrix numeric
## oob.times 3221 -none- numeric
## classes 2 -none- character
## importance 57 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3221 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## param 2 -none- listPrediction - Test Data - Random Forest (OOB)
vctRndForoob <- predict(mdlRndForoob, newdata=dfrTstData)
cmxRndForoob <- confusionMatrix(vctRndForoob, dfrTstData$Result)
cmxRndForoob## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam spam
## Not Spam 807 28
## spam 39 506
##
## Accuracy : 0.9514
## 95% CI : (0.9387, 0.9622)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8981
## Mcnemar's Test P-Value : 0.2218
##
## Sensitivity : 0.9539
## Specificity : 0.9476
## Pos Pred Value : 0.9665
## Neg Pred Value : 0.9284
## Prevalence : 0.6130
## Detection Rate : 0.5848
## Detection Prevalence : 0.6051
## Balanced Accuracy : 0.9507
##
## 'Positive' Class : Not Spam
## ** We predict the model using the OOB model and get an accuracy of 95% **
Thank You
print("Thank You")## [1] "Thank You"Analysis Using the 4 methods for creation of Random Forest we get the following accuracy Random Forest(default)- 95.29% CV - 95% RepeatedCV- 94.86% OOB - 95.14%
As by using the Random forest(default) method we get the highest accuracy we will use this model further for the dataset