Advanced Algorithms & Evaluation Metrics

October 6, 2026 machine-learning

Handwritten notes on advanced ML algorithms and evaluation: ensemble methods, SVMs, tree-based models, ROC curves, and performance metrics.

These notes dive into advanced machine learning algorithms and how to properly evaluate them:

Tree-Based Models: Decision trees, random forests, gradient boosting, and XGBoost.
Support Vector Machines: Kernel trick, margin maximization, soft margins, and non-linear classification.
Ensemble Methods: Bagging, boosting, stacking, and why ensembles outperform individual models.
Evaluation Metrics: ROC-AUC, precision-recall curves, F1 score, log loss, and cross-validation strategies.