Evaluating Feature Importance in Random Forest with PermutationImportance

This function uses Eli5's PermutationImportance to evaluate the importance of features in a random forest model. It first creates a random forest classifier, then trains the model, and uses PermutationImportance to calculate the importance scores for each feature.

Technology Stack : Eli5, sklearn

Code Type : Machine learning

Code Difficulty : Intermediate

                
                    
import numpy as np
import eli5
from eli5.sklearn import PermutationImportance

def random_eli5_function(X, y):
    # Generate a random classifier
    classifier = eli5.sklearn.RandomForestClassifier(n_estimators=10, random_state=42)
    
    # Train the classifier
    classifier.fit(X, y)
    
    # Create a PermutationImportance object
    perm = PermutationImportance(classifier, random_state=42).fit(X, y)
    
    # Get the importance scores
    importance_scores = perm.importances_mean_
    
    return importance_scores