Answer the following questions

a. Explain what is decision tree with an example diagram and briefly describe how to find the split node and split criteria?

b. Explain what is bagging and boosting. Give an example to both using decision trees

c. Describe in detail with steps how to construct and train a random forest. T

d. rain a random forest using R. For the dataset which can be obtained by running following r script, run a random forest classifier to predict the raw score on the reading set

library(party)

data("readingSkills")

head(readingSkills)

A data frame with 200 observations on the following 4 variables. nativeSpeaker: factor with levels no and yes, where yes indicates that the child is a native speaker of the language of the reading test. Age ; age of the child in years. shoeSize: shoe size of the child in cm. Score: raw score on the reading test.