Advanced Machine Learning with Python | Hyperparameter Tuning, Cross-Validation, and Random Forests

Welcome back In this video, we take our machine learning journey a step further. Building on our previous tutorial, we'll explore advanced techniques like hyperparameter tuning, cross-validation, and delve into ensemble methods with a focus on Random Forests using Python and scikit-learn.

🔍 What You'll Learn:

What are ensemble methods and their benefits
Building and evaluating a Random Forest model
Hyperparameter tuning with GridSearchCV for Random Forests
Performing cross-validation to assess model performance
📚 Resources:

scikit-learn Documentation

💻 Code Snippets:

python
Copy code

# Initialize and train the Random Forest classifier
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
accuracy_rf = accuracy_score(y_test, y_pred_rf)
print(f'Random Forest Accuracy: {accuracy_rf * 100:.2f}%')

# Hyperparameter Tuning with GridSearchCV
param_grid_rf = {
'n_estimators': [50, 100, 200],
'max_features': ['auto', 'sqrt', 'log2'],
'max_depth': [None, 10, 20, 30]
}
grid_search_rf = GridSearchCV(RandomForestClassifier(random_state=42), param_grid_rf, cv=5)
print(f'Best parameters for Random Forest: {best_params_rf}')

# Evaluate the tuned Random Forest classifier
rf_classifier_tuned = RandomForestClassifier(
n_estimators=best_params_rf['n_estimators'],
max_features=best_params_rf['max_features'],
max_depth=best_params_rf['max_depth'],
random_state=42
)
accuracy_rf_tuned = accuracy_score(y_test, y_pred_rf_tuned)
print(f'Tuned Random Forest Accuracy: {accuracy_rf_tuned * 100:.2f}%')
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