Classical Machine Learning
2026
5
- AutoGluon: Simplifying Machine Learning Baselines to a Few Lines of Code
- Training with Imbalanced Samples: From Statistical Learning to Long-Tail Learning
- From Bagging to Stacking: A Map of Ensemble Learning Methods
- Shapley and SHAP: State-of-the-Art Tools for Model Interpretability
- Tree-Based Models Are Still SOTA for Tabular Data: XGBoost, LightGBM, and CatBoost
2025
1
2024
4
- Text Embedding: From Bag-of-Words to Qwen3 Embedding
- Machine Learning Supplementary Topics: Multiclass Learning, Class Imbalance, and Clustering Evaluation
- Interpretable Machine Learning: Model Explanations, SHAP, and Counterfactual Methods
- Feature Engineering: Feature Selection, Feature Construction, and Dimensionality Reduction