algs
This repository contains python implementations of a handful of statistical learning algorithms that appear in An Introduction to Statistical Learning [1] and The Elements of Statistical Learning [2].
Quickstart
-
git clone git@github.com:coxy1989/algs.git -
cd algs -
conda env create -f environment.yml -
source activate algs -
jupyter notebook
Run the notebooks
- Linear Regression
- Logistic Regression
- Linear Discriminant Analysis
- Quadratic Discriminant Analysis
- K-Fold Cross Validation
- The Bootstrap
- Best Subset Selection
- Forward and Backward Stepwise Selection
- Ridge Regression
- Trees, Bagging, Random Forests and Boosting
- Perceptron
- PCA
- K-Means
References
[1] Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. New York Springer, 2013.
[2] Hastie, T., Tibshirani, R.,, Friedman, J. The Elements of Statistical Learning. Springer New York Inc. 2001.
This repository was split out from mlsabattical on 09/01/2018