Decision Tree

A sample training dataset for decision trees is given as follows.

$\backslash$table$[[$Feature$1,$Feature$2,$Feature$3,$Feature$4,$Class$],[$Stronq$,$Low,Apple,Medium,$0],[$Weak$,$Low,Oranqe,Hiqh,$1],[$Strong$,$Medium,Banana,Low,$0],[$Stronq$,$High,Apple,Medium,$1],[$Stronq$,$Medium,Oranqe,High,$0],[$Weak$,$Low,Banana,Low,$1],[$Stronq$,$Medium,Apple,Hiqh,$0],[$Weak$,$High,Banana,Medium,$1],[$Weak$,$Low,Orange,Low,$0],[$Stronq$,$Hiqh,Banana,High,$1],[$Stronq$,$Low,Apple,Medium,$0],[$Weak$,$Medium,Oranqe,High,$1],[$Weak$,$High,Banana,Low,$0],[$Stronq$,$Medium,Apple,Hiqh,$1],[$Weak$,$Low,Orange,Medium,$0]]$

a$)$ Using this data set, create the most appropriate Decision Tree by making Information Gain and entropy calculations. $($Show all steps and calculations$)$

b$)$ Draw this decision tree.

c$)$ When you consider the following Test Data set

Show the Confusion Matrix?

Calculate the Recall Value?

Calculate the Precision Value

$\backslash$table$[[$Feature$1,$Feature$2,$Feature$3,$Feature$4,$Class$],[$Strong$,$Low,Banana,Hiqh,$1],[$Weak$,$Medium,Apple,Hiqh,$0],[$Strong$,$High,Banana,Medium,$1],[$Weak$,$Medium,Banana,High,$0],[$Stronq$,$Medium,Apple,Low,$0],[$Weak$,$Low,Oranqe,Medium,$0]]$