Random Forest Classification Accuracy Calculation

This function uses the Random Forest algorithm to classify the given feature set X and label set y, and returns the accuracy of the model on the test set.

Technology Stack : scikit-learn

Code Type : Machine learning classification function

Code Difficulty : Intermediate

                
                    
def random_forest_classification(X, y):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score

    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

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

    # Train the model
    clf.fit(X_train, y_train)

    # Make predictions on the test set
    y_pred = clf.predict(X_test)

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

    return accuracy