In this program
- Data preparation
- Supervised learning
- Unsupervised learning
Supervised learning
Data mining · Standard syllabus
Classification and regression trees
Objectives
- 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
Study materials
- Study guideComing soon
- Exam StrategyComing soon
- Common MistakesComing soon
- WorksheetsComing soon
- Word problemsComing soon
- Mixed PracticeComing soon
- Multi-Unit ProblemsComing soon
- ReviewComing soon
- Practice testComing soon
- Answer keyComing soon
Interactive practice
Quizzes, typed answers, and flashcards for this unit — coming soon.
- Coming soon
Quiz
Multiple-choice questions with instant feedback
- Coming soon
Typed practice
Type answers and check them
- Coming soon
Flashcards
Vocabulary and key facts
- Coming soon
Mixed quiz
Harder mixed review for this standard