gc()
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 357617 19.1 592000 31.7 460000 24.6
## Vcells 549450 4.2 1023718 7.9 822639 6.3
ls()
## character(0)
rm(list=ls())
library(data.table)
library(dplyr)
## -------------------------------------------------------------------------
## data.table + dplyr code now lives in dtplyr.
## Please library(dtplyr)!
## -------------------------------------------------------------------------
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:data.table':
##
## between, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
getwd()
## [1] "C:/Users/Sonal/Documents"
## Loading the package
library(e1071)
head(iris,5)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## Attach the Data
attach(iris)
# Divide Iris data to x (containt the all features) and y only the classes
x <- subset(iris, select=-Species)
x
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1
## 11 5.4 3.7 1.5 0.2
## 12 4.8 3.4 1.6 0.2
## 13 4.8 3.0 1.4 0.1
## 14 4.3 3.0 1.1 0.1
## 15 5.8 4.0 1.2 0.2
## 16 5.7 4.4 1.5 0.4
## 17 5.4 3.9 1.3 0.4
## 18 5.1 3.5 1.4 0.3
## 19 5.7 3.8 1.7 0.3
## 20 5.1 3.8 1.5 0.3
## 21 5.4 3.4 1.7 0.2
## 22 5.1 3.7 1.5 0.4
## 23 4.6 3.6 1.0 0.2
## 24 5.1 3.3 1.7 0.5
## 25 4.8 3.4 1.9 0.2
## 26 5.0 3.0 1.6 0.2
## 27 5.0 3.4 1.6 0.4
## 28 5.2 3.5 1.5 0.2
## 29 5.2 3.4 1.4 0.2
## 30 4.7 3.2 1.6 0.2
## 31 4.8 3.1 1.6 0.2
## 32 5.4 3.4 1.5 0.4
## 33 5.2 4.1 1.5 0.1
## 34 5.5 4.2 1.4 0.2
## 35 4.9 3.1 1.5 0.2
## 36 5.0 3.2 1.2 0.2
## 37 5.5 3.5 1.3 0.2
## 38 4.9 3.6 1.4 0.1
## 39 4.4 3.0 1.3 0.2
## 40 5.1 3.4 1.5 0.2
## 41 5.0 3.5 1.3 0.3
## 42 4.5 2.3 1.3 0.3
## 43 4.4 3.2 1.3 0.2
## 44 5.0 3.5 1.6 0.6
## 45 5.1 3.8 1.9 0.4
## 46 4.8 3.0 1.4 0.3
## 47 5.1 3.8 1.6 0.2
## 48 4.6 3.2 1.4 0.2
## 49 5.3 3.7 1.5 0.2
## 50 5.0 3.3 1.4 0.2
## 51 7.0 3.2 4.7 1.4
## 52 6.4 3.2 4.5 1.5
## 53 6.9 3.1 4.9 1.5
## 54 5.5 2.3 4.0 1.3
## 55 6.5 2.8 4.6 1.5
## 56 5.7 2.8 4.5 1.3
## 57 6.3 3.3 4.7 1.6
## 58 4.9 2.4 3.3 1.0
## 59 6.6 2.9 4.6 1.3
## 60 5.2 2.7 3.9 1.4
## 61 5.0 2.0 3.5 1.0
## 62 5.9 3.0 4.2 1.5
## 63 6.0 2.2 4.0 1.0
## 64 6.1 2.9 4.7 1.4
## 65 5.6 2.9 3.6 1.3
## 66 6.7 3.1 4.4 1.4
## 67 5.6 3.0 4.5 1.5
## 68 5.8 2.7 4.1 1.0
## 69 6.2 2.2 4.5 1.5
## 70 5.6 2.5 3.9 1.1
## 71 5.9 3.2 4.8 1.8
## 72 6.1 2.8 4.0 1.3
## 73 6.3 2.5 4.9 1.5
## 74 6.1 2.8 4.7 1.2
## 75 6.4 2.9 4.3 1.3
## 76 6.6 3.0 4.4 1.4
## 77 6.8 2.8 4.8 1.4
## 78 6.7 3.0 5.0 1.7
## 79 6.0 2.9 4.5 1.5
## 80 5.7 2.6 3.5 1.0
## 81 5.5 2.4 3.8 1.1
## 82 5.5 2.4 3.7 1.0
## 83 5.8 2.7 3.9 1.2
## 84 6.0 2.7 5.1 1.6
## 85 5.4 3.0 4.5 1.5
## 86 6.0 3.4 4.5 1.6
## 87 6.7 3.1 4.7 1.5
## 88 6.3 2.3 4.4 1.3
## 89 5.6 3.0 4.1 1.3
## 90 5.5 2.5 4.0 1.3
## 91 5.5 2.6 4.4 1.2
## 92 6.1 3.0 4.6 1.4
## 93 5.8 2.6 4.0 1.2
## 94 5.0 2.3 3.3 1.0
## 95 5.6 2.7 4.2 1.3
## 96 5.7 3.0 4.2 1.2
## 97 5.7 2.9 4.2 1.3
## 98 6.2 2.9 4.3 1.3
## 99 5.1 2.5 3.0 1.1
## 100 5.7 2.8 4.1 1.3
## 101 6.3 3.3 6.0 2.5
## 102 5.8 2.7 5.1 1.9
## 103 7.1 3.0 5.9 2.1
## 104 6.3 2.9 5.6 1.8
## 105 6.5 3.0 5.8 2.2
## 106 7.6 3.0 6.6 2.1
## 107 4.9 2.5 4.5 1.7
## 108 7.3 2.9 6.3 1.8
## 109 6.7 2.5 5.8 1.8
## 110 7.2 3.6 6.1 2.5
## 111 6.5 3.2 5.1 2.0
## 112 6.4 2.7 5.3 1.9
## 113 6.8 3.0 5.5 2.1
## 114 5.7 2.5 5.0 2.0
## 115 5.8 2.8 5.1 2.4
## 116 6.4 3.2 5.3 2.3
## 117 6.5 3.0 5.5 1.8
## 118 7.7 3.8 6.7 2.2
## 119 7.7 2.6 6.9 2.3
## 120 6.0 2.2 5.0 1.5
## 121 6.9 3.2 5.7 2.3
## 122 5.6 2.8 4.9 2.0
## 123 7.7 2.8 6.7 2.0
## 124 6.3 2.7 4.9 1.8
## 125 6.7 3.3 5.7 2.1
## 126 7.2 3.2 6.0 1.8
## 127 6.2 2.8 4.8 1.8
## 128 6.1 3.0 4.9 1.8
## 129 6.4 2.8 5.6 2.1
## 130 7.2 3.0 5.8 1.6
## 131 7.4 2.8 6.1 1.9
## 132 7.9 3.8 6.4 2.0
## 133 6.4 2.8 5.6 2.2
## 134 6.3 2.8 5.1 1.5
## 135 6.1 2.6 5.6 1.4
## 136 7.7 3.0 6.1 2.3
## 137 6.3 3.4 5.6 2.4
## 138 6.4 3.1 5.5 1.8
## 139 6.0 3.0 4.8 1.8
## 140 6.9 3.1 5.4 2.1
## 141 6.7 3.1 5.6 2.4
## 142 6.9 3.1 5.1 2.3
## 143 5.8 2.7 5.1 1.9
## 144 6.8 3.2 5.9 2.3
## 145 6.7 3.3 5.7 2.5
## 146 6.7 3.0 5.2 2.3
## 147 6.3 2.5 5.0 1.9
## 148 6.5 3.0 5.2 2.0
## 149 6.2 3.4 5.4 2.3
## 150 5.9 3.0 5.1 1.8
y<-Species
y
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
## Create SVM Model and show summary
svm_model <- svm(Species ~ ., data=iris)
summary(svm_model)
##
## Call:
## svm(formula = Species ~ ., data = iris)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
## gamma: 0.25
##
## Number of Support Vectors: 51
##
## ( 8 22 21 )
##
##
## Number of Classes: 3
##
## Levels:
## setosa versicolor virginica
## Run Prediction and you can measuring the execution time in R
pred <- predict(svm_model,x)
system.time(pred <- predict(svm_model,x))
## user system elapsed
## 0 0 0
## See the confusion matrix result of prediction, using command table to
##compare the result of SVM prediction and the class data in y variable.
table(pred,y)
## y
## pred setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 48 2
## virginica 0 2 48
## Tuning SVM to find the best cost and gamma.
svm_tune <- tune(svm, train.x=x, train.y=y,
kernel="radial", ranges=list(cost=10^(-1:2), gamma=c(.5,1,2)))
print(svm_tune)
##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost gamma
## 1 0.5
##
## - best performance: 0.04666667
##After we find the best cost and gamma, we can create svm model again and try to run again
svm_model_after_tune <- svm(Species ~ ., data=iris, kernel="radial", cost=1, gamma=0.5)
summary(svm_model_after_tune)
##
## Call:
## svm(formula = Species ~ ., data = iris, kernel = "radial", cost = 1,
## gamma = 0.5)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
## gamma: 0.5
##
## Number of Support Vectors: 59
##
## ( 11 23 25 )
##
##
## Number of Classes: 3
##
## Levels:
## setosa versicolor virginica
##Run Prediction again with new model
pred <- predict(svm_model_after_tune,x)
system.time(predict(svm_model_after_tune,x))
## user system elapsed
## 0.01 0.00 0.02
## The confusion matrix result of prediction, using command table
##to compare the result of SVM prediction and the class data in y variable.
table(pred,y)
## y
## pred setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 48 2
## virginica 0 2 48
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
data(Boston)
attach(Boston)
##Plot the data
plot(medv,rm, pch=16)
plot(medv,ptratio, pch=16)
##Create a linear regression model
model <- lm(medv ~ rm+ptratio, Boston)
summary(model)
##
## Call:
## lm(formula = medv ~ rm + ptratio, data = Boston)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.672 -2.821 0.102 2.770 39.819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.5612 4.1889 -0.611 0.541
## rm 7.7141 0.4136 18.650 <2e-16 ***
## ptratio -1.2672 0.1342 -9.440 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.104 on 503 degrees of freedom
## Multiple R-squared: 0.5613, Adjusted R-squared: 0.5595
## F-statistic: 321.7 on 2 and 503 DF, p-value: < 2.2e-16
##Add the fitted line
plot(medv, pch=16)
abline(model)
## Warning in abline(model): only using the first two of 3 regression
## coefficients
##Make a prediction for each X
predictedY <- predict(model, Boston)
## Display the predictions
points(medv, predictedY, col = "blue", pch=4)
##We come with the following code to compute the RMSE
??rmse
## starting httpd help server ...
## done
error <- model$residuals
rmse <- function(error)
{
sqrt(mean(error^2))
}
predictionRMSE <- rmse(error)
predictionRMSE
## [1] 6.08595
##Support Vector Regression
model2 <- svm(medv ~ rm+ptratio, Boston)
predictedY2 <- predict(model2, Boston)
points(medv, predictedY2, col = "red", pch=4)
error2 <- model2$residuals # same as data$Y - predictedY
predictionRMSE <- rmse(error2)
predictionRMSE
## [1] 5.167693
## Tuning the result
tuneResult <- tune(svm, medv ~ rm+ptratio, data = Boston,
ranges = list(epsilon = seq(0,0.2,0.01), cost = 2^(2:9)))
tuneResult
##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## epsilon cost
## 0.18 4
##
## - best performance: 28.4981
plot(tuneResult)
tunedModel <- tuneResult$best.model
tunedModelY <- predict(tunedModel, Boston)
error <- tuneResult$best.model$residuals
tunedModelRMSE <- rmse(error)
tunedModelRMSE
## [1] 5.057808
## Tuning the Result
tuneResult <- tune(svm, medv ~ rm+ptratio, data = Boston,
ranges = list(epsilon = seq(0.1,0.2,0.01), cost = 2^(2:7)))
print(tuneResult)
##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## epsilon cost
## 0.2 4
##
## - best performance: 28.65271
plot(tuneResult)
