Random Forest Classification Accuracy Calculation

This function uses the RandomForestClassifier to fit the training data and predict labels on the test data, returning the model's accuracy.

Technology Stack : scikit-learn

Code Type : Machine learning classification

Code Difficulty : Intermediate

                
                    
def random_forest_classification(X_train, y_train, X_test):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import accuracy_score

    # Initialize the RandomForestClassifier
    clf = RandomForestClassifier(n_estimators=100, random_state=42)

    # Fit the model on the training data
    clf.fit(X_train, y_train)

    # Predict the labels on the test data
    y_pred = clf.predict(X_test)

    # Calculate the accuracy of the model
    accuracy = accuracy_score(y_test, y_pred)

    return accuracy