Evaluating Feature Importance with Logistic Regression and PermutationImportance

This function fits a logistic regression model on the given data and uses eli5's PermutationImportance to evaluate feature importance.

Technology Stack : scikit-learn, eli5, LogisticRegression, PermutationImportance

Code Type : Python Function

Code Difficulty : Intermediate

                
                    
import random
from sklearn.linear_model import LogisticRegression
from eli5.sklearn import PermutationImportance

def random_eli5_function(X, y):
    # Initialize the model
    model = LogisticRegression()
    
    # Fit the model
    model.fit(X, y)
    
    # Create a PermutationImportance object
    perm = PermutationImportance(model, random_state=1)
    
    # Fit the PermutationImportance object
    perm.fit(X, y)
    
    # Return the importance scores
    return perm.importances_mean_

# Code Explanation
"""
This function fits a logistic regression model on the given data X and y.
Then, it creates a PermutationImportance object to evaluate the feature importances.
Finally, it returns the mean importance scores of the features.
"""

# Technical Stack Explanation
"""
The code uses the scikit-learn library for logistic regression and eli5 library for feature importance analysis.
It involves the following packages and technologies:
- scikit-learn: A Python machine learning library.
- eli5: A library for interpreting machine learning models.
- LogisticRegression: A classifier that applies logistic regression.
- PermutationImportance: A method to evaluate feature importance in a model.
"""

# Technical Stack Explanation in English
"""
The code uses the scikit-learn library for logistic regression and eli5 library for feature importance analysis.
It involves the following packages and technologies:
- scikit-learn: A Python machine learning library.
- eli5: A library for interpreting machine learning models.
- LogisticRegression: A classifier that applies logistic regression.
- PermutationImportance: A method to evaluate feature importance in a model.
"""