Overview
Boosting is a powerful ensemble technique that combines multiple weak learners to form a strong learner. XGBoost (Extreme Gradient Boosting) is one of the most popular and performant boosting algorithms, widely used in Kaggle competitions and industry.
Today, you'll learn how Boosting works, and apply XGBoost to a real dataset, using hyperparameter tuning and performance evaluation techniques.
Key Concepts
- Boosting vs Bagging
- Gradient Boosting Algorithm
- XGBoost basics and advantages
- Hyperparameters: learning_rate, n_estimators, max_depth
- Early stopping & overfitting control
Practice Exercise
Exercise: Predict Titanic Survival with XGBoost
- Use the Titanic dataset from Kaggle or sklearn.
- Preprocess the dataset (handle missing values, encode categoricals).
- Train a baseline XGBoost model using
xgboost.XGBClassifier. - Tune key hyperparameters:
n_estimators,max_depth,learning_rate. - Use
GridSearchCVorRandomizedSearchCVwith cross-validation. - Visualize feature importance using
xgb.plot_importance().
Resources
XGBoost with Scikit-learn Guide
Main resource for today
XGBoost Documentation
Official XGBoost Python API docs
XGBoost Parameters Explained
Overview of all tunable parameters
Titanic with XGBoost - Sample Notebook
Hands-on example notebook for using XGBoost
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