Random Model Feature Importance Evaluation with eli5

This function uses PermutationImportance from the eli5 library to evaluate the feature importance of a randomly selected sklearn model. It first selects a model, then fits PermutationImportance to the model, and finally returns the feature importance.

Technology Stack : eli5, sklearn, PermutationImportance

Code Type : The type of code

Code Difficulty : Advanced

                
                    
import random
import eli5
from eli5.sklearn import PermutationImportance

def random_eli5_function(X, y):
    # This function uses the PermutationImportance class from the eli5 library to evaluate feature importance in a random sklearn model.
    # The model used here is a RandomForestClassifier, which is randomly selected from the eli5 library.

    # Randomly select a model from eli5's sklearn module
    available_models = eli5.sklearn.__all__
    model_name = random.choice(available_models)
    model = getattr(eli5.sklearn, model_name)

    # Initialize the model with random parameters (if applicable)
    if model_name == 'RandomForestClassifier':
        model = model(n_estimators=10, random_state=42)
    elif model_name == 'LogisticRegression':
        model = model(penalty='l2', C=1.0, random_state=42)

    # Fit the model to the data
    model.fit(X, y)

    # Initialize PermutationImportance
    perm = PermutationImportance(model, random_state=42)

    # Fit PermutationImportance to the model
    perm.fit(X, y)

    # Get feature importance
    feature_importance = perm.feature_importances_

    return feature_importance