Exploring Eli5 Functions with Machine Learning Models

PermutationImportance, Explain_weights, Explainer, FeatureImportances

Technology Stack : Packages and technologies used in the code

Code Type : The type of code

Code Difficulty :

                
                    
import random
import eli5
from eli5.sklearn import PermutationImportance

def random_eli5_function():
    # Randomly select an Eli5 function and use it
    selected_function = random.choice([
        "PermutationImportance",
        "Explain_weights",
        "Explainer",
        "FeatureImportances"
    ])
    
    # Randomly select a model and data to demonstrate the function
    selected_model = random.choice([
        "RandomForestClassifier",
        "LogisticRegression",
        "GradientBoostingClassifier"
    ])
    
    # Randomly generate some dummy data
    from sklearn.datasets import load_iris
    data = load_iris()
    X, y = data.data, data.target
    
    # Use the selected function with the selected model and data
    if selected_function == "PermutationImportance":
        explainer = PermutationImportance(estimator=selected_model, random_state=42)
        explainer.fit(X, y)
        feature_importances = explainer.feature_importances_
    elif selected_function == "Explain_weights":
        explainer = eli5.explain_weights(estimator=selected_model, X=X, y=y)
        feature_importances = explainer.feature_importances_
    elif selected_function == "Explainer":
        explainer = eli5.Explainer(selected_model, X)
        explainer.fit(X, y)
        feature_importances = explainer.feature_importances_
    elif selected_function == "FeatureImportances":
        explainer = eli5.FeatureImportances(selected_model, X)
        explainer.fit(X, y)
        feature_importances = explainer.feature_importances_
    
    return feature_importances