Random Forest Classification Model Accuracy Evaluation

This function uses the RandomForestClassifier to train a model and evaluate its accuracy on test data. It first initializes a classifier using the RandomForestClassifier from the Scikit-learn library. Then, it fits the model on the training data. Next, it makes predictions on the test data and calculates the accuracy of the model.

Technology Stack : Scikit-learn, RandomForestClassifier, accuracy_score

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)

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

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

    return accuracy