🔥🔥 TOP 10 CRITICAL Priority - Most popular ML algorithm #10

Overview

Implement Gradient Boosting Machine (GBM), the most decisive algorithm in Kaggle competitions and industry ML pipelines.

Implementation Details

• Sequential ensemble of weak learners (decision trees)
• Gradient descent in function space
• Learning rate (shrinkage)
• Tree depth control
• Subsampling for regularization
• Early stopping

Variants (Priority Order)

1. Basic GBM - Core algorithm
2. GBDT - Gradient Boosted Decision Trees
3. (Future) XGBoost-style optimizations
4. (Future) LightGBM-style leaf-wise growth

References

• "XGBoost is the decisive choice between winning and losing in Kaggle competitions"
• Superior to Random Forest with proper tuning
• State-of-the-art for tabular data

Acceptance Criteria

• GradientBoostingClassifier struct
• GradientBoostingRegressor struct
• fit/predict/staged_predict
• Feature importance
• Early stopping
• Comprehensive tests (EXTREME TDD)
• Example: gbm_boston_housing.rs
• Book chapter: ml-fundamentals/gradient-boosting.md

Priority Justification

Gradient Boosting is the #1 algorithm for winning ML competitions

• Kaggle winners use GBM/XGBoost in 90%+ of competitions
• Industry standard for structured/tabular data
• Outperforms Random Forest and Neural Networks on most tasks

Complexity Warning

⚠️ This is a complex algorithm requiring:

• Decision tree integration
• Gradient computation
• Loss function derivatives
• ~500-800 LOC implementation