In this program
- Classification and regression trees
- Ensemble methods: bagging
- Random forests
- Boosting (AdaBoost, gradient boosting intro)
- k-nearest neighbors and naive Bayes
- Model evaluation: ROC and AUC
- Confusion matrices
Supervised learning
Boosting (AdaBoost, gradient boosting intro)
Data mining · STEM / applied
Topic
Boosting (AdaBoost, gradient boosting intro)
Undergraduate Data mining — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).