Applied Machine Learning | Session 21 | Gradient Boosting, Random Forest & Model Stacking

Описание: We dive deep into advanced ensemble techniques using hands-on Python:

🌲 Random Forest – understand bagging with decision trees

🎯 Gradient Boosting – learn boosting methods for strong predictive models

🧩 Model Stacking – combine multiple models for better performance

🚀 End-to-end process: load data, feature engineer, preprocess

📈 Train and evaluate each model using accuracy, confusion matrix, precision, recall

🛡️ Detect and avoid overfitting with training vs. validation comparison

🔧 Tools & Libraries:
Python • pandas • NumPy • scikit-learn • XGBoost • matplotlib • seaborn

🔗 View the full notebook here:
https://github.com/GenEd-Tech/Applied...

Perfect for learners wanting to build ensemble models step-by-step. No fluff—just clear, practical ML in action!

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