A regression model predicts a value $$Y$$ given a set of $$X$$ variables. In the regression model $$Y \sim X$$, we measure the effect of $$X$$ on $$Y$$, i.e., we measure the association between changes in values of $$X$$ and changes in values of $$Y$$. Analyzing a regression model is a method used to solve a regression problem.

Reference: James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An introduction to statistical learning: with applications in R. New York: Springer.

Regression Model

- Regression Models
- Decision Tree Models
- Random Forest Models
- Support Vector Machine
- Naive Bayes
- K Means
- KNN

University of Michigan - Ann Arbor

Johns Hopkins University

Methods of supervised statistical learning

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

Classical SL Methods (examples)

- GAM
- Boosting
- Support vector machines

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