Track 2 · Path · 8 explainers
Classic ML
Trees, SVMs, clustering, and ensemble methods beyond neural nets
- 1
Decision Trees & Splitting
Recursive partitioning, Gini impurity, and pruning
- 2
K-Nearest Neighbors
Classification by proximity and distance metrics
- 3
Support Vector Machines
Maximum margin classifiers and the kernel trick
- 4
Naive Bayes Classifier
Probabilistic classification with Bayes' rule
- 5
K-Means Clustering
Unsupervised grouping by centroid distance
- 6
Bagging & Random Forests
Bootstrap aggregation and decorrelated tree ensembles
- 7
Gradient Boosting
Sequential residual fitting for powerful predictions
- 8
Ensemble Methods & Stacking
Voting, stacking, and combining diverse models