Random Forest Classification Accuracy Calculation

This function trains a random forest classifier on the given data and calculates the accuracy on the test set.

Technology Stack : Scikit-learn, NumPy

Code Type : The type of code

Code Difficulty :

                
                    
import numpy as np
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

def random_forest_classification(X, y, test_size=0.3, random_state=42):
    """
    This function generates a random forest classifier model and trains it on the given data.
    """
    # Split the data into training and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
    
    # Create a random forest classifier
    clf = RandomForestClassifier(n_estimators=100, random_state=random_state)
    
    # Train the classifier
    clf.fit(X_train, y_train)
    
    # Predict the labels for the test set
    y_pred = clf.predict(X_test)
    
    # Calculate the accuracy of the classifier
    accuracy = clf.score(X_test, y_test)
    
    return accuracy

# Example usage:
# X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=42)
# accuracy = random_forest_classification(X, y)
# print(f"Accuracy: {accuracy}")