Mutual information for feature selection in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function for mutual information classification.

ML Python

from sklearn.feature_selection import [1]

Drag options to blanks, or click blank then click option'

Amutual_info_regression

Bmutual_info_classif

CSelectKBest

Dchi2

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing mutual_info_regression which is for regression tasks.

Choosing SelectKBest which is a selector, not the mutual information function.

The function mutual_info_classif computes mutual information for classification tasks, which helps select important features.

2fill in blank

medium

Complete the code to calculate mutual information scores for features X and target y.

ML Python

mi_scores = [1](X, y)

Drag options to blanks, or click blank then click option'

Amutual_info_classif

Bf_classif

Cchi2

Dmutual_info_regression

Attempts:

3 left

💡 Hint

Common Mistakes

Using mutual_info_regression which is for regression problems.

Using chi2 or f_classif which are different feature scoring methods.

mutual_info_classif calculates mutual information scores between features and a classification target.

3fill in blank

hard

Fix the error in the code to select top 3 features based on mutual information scores.

ML Python

from sklearn.feature_selection import SelectKBest
selector = SelectKBest(score_func=[1], k=3)
X_new = selector.fit_transform(X, y)

Drag options to blanks, or click blank then click option'

Amutual_info_regression

Bchi2

Cmutual_info_classif

Df_regression

Attempts:

3 left

💡 Hint

Common Mistakes

Using chi2 which requires non-negative features.

Using mutual_info_regression which is for regression.

Use mutual_info_classif as the scoring function for classification feature selection.

4fill in blank

hard

Fill both blanks to create a dictionary of feature names and their mutual information scores, filtering scores greater than 0.1.

ML Python

mi_dict = {feature: [1] for feature, score in zip(feature_names, mi_scores) if score [2] 0.1}

Drag options to blanks, or click blank then click option'

Ascore

B>

C<

Dfeature

Attempts:

3 left

💡 Hint

Common Mistakes

Using feature instead of score as dictionary value.

Using '<' instead of '>' in the condition.

The dictionary maps each feature to its score, including only scores greater than 0.1.

5fill in blank

hard

Fill both blanks to select top 5 features using mutual information and transform the dataset.

ML Python

selector = SelectKBest(score_func=[1], k=[2])
selected_features = selector.fit_transform(X, y)
selected_feature_names = [name for i, name in enumerate(feature_names) if selector.get_support()[i]]

Drag options to blanks, or click blank then click option'

Amutual_info_classif

B3

Cindex

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong scoring function.

Selecting wrong number of features.

Use mutual_info_classif as the scoring function, select top 5 features.

Practice

1. What does mutual information measure in feature selection?

easy

A. The amount of shared information between a feature and the target variable

B. The correlation coefficient between two features

C. The difference between feature means

D. The number of missing values in a feature

Solution

1. Step 1: Understand mutual information concept

   Mutual information measures how much knowing one variable reduces uncertainty about another.

2. Step 2: Apply to feature selection context

   In feature selection, it measures how much information a feature shares with the target variable.

3. Final Answer:

   The amount of shared information between a feature and the target variable -> Option A

4. Quick Check:

   Mutual information = shared info [OK]

Hint: Mutual info = shared info between feature and target [OK]

Common Mistakes:

• Confusing mutual information with correlation
• Thinking it measures missing data
• Assuming it measures difference in means

2. Which Python function is used to compute mutual information for classification tasks?

easy

A. mutual_info_classif

B. mutual_info_regression

C. mutual_info_score

D. mutual_info_classifier

Solution

1. Step 1: Recall mutual information functions in sklearn

   For classification, sklearn provides mutual_info_classif.

2. Step 2: Differentiate from regression function

   mutual_info_regression is for regression, not classification.

3. Final Answer:

   mutual_info_classif -> Option A

4. Quick Check:

   Classification uses mutual_info_classif [OK]

Hint: Classification uses mutual_info_classif function [OK]

Common Mistakes:

• Using mutual_info_regression for classification
• Confusing function names
• Assuming mutual_info_score exists in sklearn

3. Given this code snippet, what is the output?

from sklearn.feature_selection import mutual_info_classif
import numpy as np
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])
y = np.array([0, 1, 0, 1])
mi = mutual_info_classif(X, y, discrete_features=[True, True])
print(np.round(mi, 2))

medium

A. [0.0 0.0]

B. [0.69 0.0]

C. [0.0 0.69]

D. [0.69 0.69]

Solution

1. Step 1: Understand input data and parameters

   X has two discrete features, y is binary. Using mutual_info_classif with discrete_features=True for both.

2. Step 2: Calculate mutual information values

   Both features vary similarly with y, so both have similar mutual information around 0.69 (close to ln(2)).

3. Final Answer:

   [0.69 0.69] -> Option D

4. Quick Check:

   Both features share info with y ~0.69 [OK]

Hint: Discrete features with binary target give ~0.69 MI if informative [OK]

Common Mistakes:

• Assuming zero mutual information for all features
• Mixing up discrete_features parameter
• Rounding errors in output

4. Identify the error in this code snippet for mutual information feature selection:

from sklearn.feature_selection import mutual_info_classif
X = [[1, 2], [2, 3], [3, 4]]
y = [0, 1, 0]
mi = mutual_info_classif(X, y)
print(mi)

medium

A. y should be a 2D array, not 1D

B. X should be a numpy array, not a list of lists

C. mutual_info_classif requires discrete_features parameter

D. mutual_info_classif cannot handle integer data

Solution

1. Step 1: Check input data types

   mutual_info_classif expects numpy arrays or similar, not plain Python lists.

2. Step 2: Identify error cause

   Passing list of lists for X can cause unexpected behavior or errors; converting to numpy array fixes this.

3. Final Answer:

   X should be a numpy array, not a list of lists -> Option B

4. Quick Check:

   Use numpy arrays for X [OK]

Hint: Always convert input data to numpy arrays before sklearn functions [OK]

Common Mistakes:

• Thinking y must be 2D
• Assuming discrete_features is always required
• Believing mutual_info_classif rejects integer data

5. You have a dataset with 10 features. After computing mutual information scores, you find two features have the highest scores but are highly correlated with each other. What is the best approach to select features?

hard

A. Select both features because they have the highest mutual information

B. Select features randomly to avoid bias

C. Select only one of the two correlated features with the highest mutual information

D. Discard both features to avoid redundancy

Solution

1. Step 1: Understand mutual information and correlation

   High mutual information means features are informative, but high correlation means redundancy.

2. Step 2: Choose features to reduce redundancy

   To avoid redundant information, select only one of the correlated features with the highest mutual information.

3. Final Answer:

   Select only one of the two correlated features with the highest mutual information -> Option C

4. Quick Check:

   Pick one correlated feature with highest MI [OK]

Hint: Avoid redundant features by picking one with highest MI [OK]

Common Mistakes:

• Selecting both correlated features causing redundancy
• Discarding informative features unnecessarily
• Choosing features randomly without criteria