Random Forest Classification Implementation

This function uses the Random Forest classifier to classify the given data. It first splits the data into training and testing sets, then trains the classifier using the Random Forest algorithm, and makes predictions on the test set.

Technology Stack : Scikit-learn

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
def random_forest_classification(data, labels):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split

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

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

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

    # Predict the labels for the test set
    predictions = clf.predict(X_test)

    return predictions