ML Using R Section 39 Grid Search

Section 39 Grid Search

df <-  read.csv("G:\\RStudio\\udemy\\ml\\Machine Learning AZ\\Part 3 - Classification\\Section 17 - Kernel SVM\\Kernel_SVM\\Social_Network_Ads.csv")
head(df)

Select the fields that we will be working with

df <- df[,3:5]
head(df)

Split dataset into training and test set (300 training, 100 test)

library(caTools)
set.seed(1234)
split <- sample.split(df$Purchased, SplitRatio = 0.75)
training_set <- subset(df, split == TRUE)
test_set <- subset(df, split == FALSE)

For Classification,it is better to do feature scaling (normalization)

# Feature Scaling 1 age, 2 is salary
training_set[,1:2] <-  scale(training_set[,1:2])
test_set[,1:2] <-  scale(test_set[,1:2])

Applying Grid Search to find the best parameters

# install.packages("caret")
library(caret)
classifier = train(form = Purchased ~ . , 
                   data = training_set, 
                   method = "svmRadial")

You are trying to do regression and your outcome only has two possible values Are you trying to do classification? If so, use a 2 level factor as your outcome column.

classifier

Support Vector Machines with Radial Basis Function Kernel 

300 samples
  2 predictor

No pre-processing
Resampling: Bootstrapped (25 reps) 
Summary of sample sizes: 300, 300, 300, 300, 300, 300, ... 
Resampling results across tuning parameters:

  C     RMSE       Rsquared 
  0.25  0.2704022  0.6852358
  0.50  0.2741952  0.6796394
  1.00  0.2804945  0.6680970

Tuning parameter 'sigma' was held constant at a value of 2.247319
RMSE was used to select the optimal model using  the smallest value.
The final values used for the model were sigma = 2.247319 and C = 0.25.

classifier$bestTune

applying sVm without using the best tune parameters:

# Create the classifier here
# install.packages("e1071")
# you can also use kernlab
library(e1071)
classifier <- svm(formula = Purchased ~ Age + EstimatedSalary,
                  data = training_set,
                  type = "C-classification",
                  kernel = "radial"
                  ) 
y_pred <-  predict(classifier, newdata=test_set[-3])
cm <- table(test_set[,3], y_pred)
cm

   y_pred
     0  1
  0 55  9
  1  4 32

applying sVm WITH using the best tune parameters:

# Create the classifier here
# install.packages("e1071")
# you can also use kernlab
library(e1071)
classifier <- svm(formula = Purchased ~ Age + EstimatedSalary,
                  data = training_set,
                  type = "C-classification",
                  kernel = "radial", 
                  cost = 0.25, 
                  cross = 10,
                  sigma = 1.22723
                  ) 
y_pred <-  predict(classifier, newdata=test_set[-3])
cm <- table(test_set[,3], y_pred)
cm

   y_pred
     0  1
  0 55  9
  1  4 32