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Master Data Science step-by-step
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Module 8
Advanced Supervised Learning: Ensemble Methods
1
Ensemble Learning: Bagging vs. Boosting
Explain the concept of ensemble learning and the difference between bagging and boosting.
2
Random Forest: Training and Feature Importance
Train a Random Forest model and interpret its feature importances.
Training Gradient Boosting Models with scikit-learn
Train a Gradient Boosting model using scikit-learn.
4
XGBoost: Beyond Gradient Boosting
Train an XGBoost model and explain its advantages over standard gradient boosting.
Decision Tree vs. Random Forest: A Performance Showdown
Compare the performance of a baseline model (e.g., Decision Tree) against an ensemble model (e.g., Random Forest).
XGBoost Hyperparameter Tuning
Tune the key hyperparameters of an XGBoost model to optimize its performance.
Previous module
Model Tuning and Selection
Next module
Unsupervised Learning: Clustering and Dimensionality Reduction