[SOLVED] Machine learning Homework I

Description

1. Pen-and-paper [12v]

Given the following decision tree learnt from 20 observation using

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.

1. 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