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
Regularization in XGBoost (L1 and L2)
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- XGBoost: A Scalable Tree Boosting System, Tianqi Chen, Carlos Guestrin, 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2939672.2939785 - Presents the fundamental design of XGBoost, explaining its objective function with regularization terms for improved generalization.
- XGBoost Parameters, XGBoost Contributors, 2024 - Provides practical details and configuration guidelines for XGBoost's regularization hyperparameters, including gamma, lambda, and alpha.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A comprehensive resource covering statistical learning methods, including regularization techniques (L1 and L2) and ensemble methods like boosting.
- Regression Shrinkage and Selection Via the Lasso, Robert Tibshirani, 1996 Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58 (Royal Statistical Society) DOI: 10.1111/j.2517-6161.1996.tb02080.x - Introduces the Lasso method, demonstrating L1 regularization and its properties for weight shrinkage and model sparsity.