Generalized additive models (GAMs) provide a general framework for
generalized additive model extending a standard linear model by allowing non-linear functions of each of the variables, while maintaining additivity. 

Generalized Additive Models

Boosting algorithms are methods to help transform weak learners to stronger learners. There are 3 common boosting algorithms used in data science:

1. AdaBoosting (Adaptive Boosting)
2. GradientBoosting
3. XGBoost (Extreme Gradient Boosting)

Common Boosting algorithm

- Generalized Additive Models
- Trees
- Random Forests
- Boosting
- Support Vector Machines
- Naive Bayes Classifier

Computer Intensive Classifiers

- GAM
- Boosting
- Support vector machines

University of Michigan - Ann Arbor

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

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

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