Getting Started with Gradient Boosting Algorithms
Chapter 1: Foundations of Ensemble Learning and Boosting
What are Ensemble Methods?
Bagging vs. Boosting
Introduction to the Boosting Principle
The AdaBoost Algorithm: A Precursor to Gradient Boosting
Understanding Weak Learners
Bias-Variance Tradeoff in Ensembles
Chapter 2: The Gradient Boosting Machine (GBM)
From Boosting to Gradient Boosting
The Role of Gradients and Residuals
The GBM Algorithm Step-by-Step
Loss Functions for Regression Tasks
Loss Functions for Classification Tasks
Hands-on Practical: Building a GBM with Python
Chapter 3: Implementing Gradient Boosting with Scikit-Learn
Scikit-Learn's GradientBoostingClassifier
Scikit-Learn's GradientBoostingRegressor
Fitting and Predicting with GBM Models
Interpreting Model Parameters
Feature Importance in GBM
Partial Dependence Plots for Model Interpretation
Hands-on Practical: Building a Predictive Model
Chapter 4: Advanced Gradient Boosting: XGBoost
Why XGBoost? Speed and Performance
Architectural Improvements over Standard GBM
Regularization in XGBoost (L1 and L2)
Handling Missing Values Automatically
Installing and Setting up XGBoost
The XGBoost API: A Walkthrough
Hands-on Practical: Training an XGBoost Model
Chapter 5: Advanced Gradient Boosting: LightGBM and CatBoost
Introduction to LightGBM: Gradient-based One-Side Sampling
LightGBM's Exclusive Feature Bundling
Introduction to CatBoost: Handling Categorical Features
CatBoost's Ordered Boosting and Symmetric Trees
Performance Comparison: XGBoost vs. LightGBM vs. CatBoost
Hands-on Practical: Implementing LightGBM and CatBoost
Chapter 6: Hyperparameter Tuning and Model Optimization
The Importance of Hyperparameter Tuning
Principal Hyperparameters in Gradient Boosting
Tuning the Number of Estimators and Learning Rate
Controlling Tree Complexity
Subsampling Parameters for Regularization
A Structured Approach to Tuning
Using Grid Search and Randomized Search
Hands-on Practical: Optimizing a Gradient Boosting Model
Introduction to CatBoost: Handling Categorical Features
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- CatBoost: Unbiased Boosting with Categorical Features, Anna G. Dorogush, Vasily Ershov, and Andrey Gulin, 2017 Advances in Neural Information Processing Systems 30, Vol. 30 (NeurIPS) DOI: 10.5591/978-1-59990-267-3_168 - The foundational paper introducing the CatBoost algorithm, its Ordered Target Statistics for categorical features, and automatic feature combinations.
- CatBoost Documentation, Yandex, 2024 (Yandex) - The official guide for installing, configuring, and using the CatBoost library, with detailed explanations of its parameters and features.
- Applied Predictive Modeling, Max Kuhn, Kjell Johnson, 2013 (Springer) DOI: 10.1007/978-1-4614-6849-3 - A resource on data preprocessing and feature engineering, including discussions on various categorical encoding methods, their pros and cons, and the risks of data leakage.