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
Understanding Weak Learners
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- A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Yoav Freund and Robert E. Schapire, 1997 Journal of Computer and System Sciences, Vol. 55 (Academic Press) DOI: 10.1006/jcss.1997.1504 - Introduces the AdaBoost algorithm, establishing the framework for combining weak learners to form a strong learner.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A standard textbook providing comprehensive coverage of boosting algorithms, weak learners (like decision stumps), and their role in the bias-variance tradeoff. (2nd edition)
- Ensemble Methods: Foundations and Algorithms, Zhi-Hua Zhou, 2012 (Chapman and Hall/CRC) DOI: 10.1201/9780429107931 - A specialized book offering extensive coverage of ensemble learning, including a detailed discussion of weak learners and their application in various boosting techniques.