R Application

# Random Forest Classification # Importing the dataset dataset = read.csv('Social_Network_Ads.csv') dataset = dataset[3:5] # Encoding the target feature as factor dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1)) # Splitting the dataset into the Training set and Test set # install.packages('caTools') library(caTools) set.seed(123) split = sample.split(dataset$Purchased, SplitRatio = 0.75) training_set = subset(dataset, split == TRUE) test_set = subset(dataset, split == FALSE) # Feature Scaling training_set[-3] = scale(training_set[-3]) test_set[-3] = scale(test_set[-3]) # Fitting Random Forest Classification to the Training set # install.packages('randomForest') library(randomForest) set.seed(123) classifier = randomForest(x = training_set[-3], y = training_set$Purchased, ntree = 500) # Predicting the Test set results y_pred = predict(classifier, newdata = test_set[-3]) # Making the Confusion Matrix cm = table(test_set[, 3], y_pred) # Visualising the Training set results library(ElemStatLearn) set = training_set X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01) X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01) grid_set = expand.grid(X1, X2) colnames(grid_set) = c('Age', 'EstimatedSalary') y_grid = predict(classifier, grid_set) plot(set[, -3], main = 'Random Forest Classification (Training set)', xlab = 'Age', ylab = 'Estimated Salary', xlim = range(X1), ylim = range(X2)) contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE) points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato')) points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3')) # Visualising the Test set results library(ElemStatLearn) set = test_set X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01) X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01) grid_set = expand.grid(X1, X2) colnames(grid_set) = c('Age', 'EstimatedSalary') y_grid = predict(classifier, grid_set) plot(set[, -3], main = 'Random Forest Classification (Test set)', xlab = 'Age', ylab = 'Estimated Salary', xlim = range(X1), ylim = range(X2)) contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE) points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato')) points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3')) # Choosing the number of trees plot(classifier)