Shannon entropy, with leaf annotations (#correct/#total)
1) [4v] Draw the training confusion matrix.
2) [3v] Identify the training F1 after a post-pruning of the given tree
under a maximum depth of 1.
3) [2v] Identify two different reasons as to why the left tree path was not further decomposed.
4) [3v] Compute the information gain of variable y1.
II. Programming [8v]
Considering the pd_speech.arff dataset available at the homework tab:
1) [6v] Using sklearn, apply a stratified 70-30 training-testing split with a fixed seed
(random_state=1), and assess in a single plot the training and testing accuracies of a decision tree
with no depth limits (and remaining default behavior) for a varying number of selected features
in {5,10,40,100,250,700}. Feature selection should be performed before decision tree learning
considering the discriminative power of the input variables according to mutual information
criterion (mutual_info_classif).
2) [2v] Why training accuracy is persistently 1? Critically analyze the gathered results.
END
P (5/7)
N (5/8)
P (3/5)
y1
y2
A
B
>2
2
