Random Forest Classification Implementation

This function uses the RandomForest algorithm to make predictions on a classification problem. It first splits the dataset into training and testing sets, then trains the model using the RandomForestClassifier, and finally predicts the results of the test set.

Technology Stack : scikit-learn

Code Type : Classification function

Code Difficulty : Intermediate

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

    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Create a RandomForestClassifier object
    clf = RandomForestClassifier(n_estimators=100, random_state=42)

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

    # Predict the labels of the test set
    y_pred = clf.predict(X_test)

    return y_pred