The Elements of Statistical Learning Notebooks – Madiyar’s Page

This page contains Jupyter notebooks implementing the algorithms found in the “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani and Jerome Friedman, proofs and summary of the textbook.

The main repository can be found at github.com/maitbayev/the-elements-of-statistical-learning.

Chapter 2

Title
2.3 Least Squares and Nearest Neighbors
2.4 Statistical Decision Theory
2.5 Local Methods in High Dimensions
2.6 Statistical Models, Supervised Learning and Function Approximation
2.7 Structured Regression Models
2.8 Classes of Restricted Estimators
2.9 Model Selection and the Bias-Variance Tradeoff
Ex. 2.8

Chapter 3

Title
3.1 Introduction
3.2 Linear Regression Models and Least Squares
3.2.2 The Gauss–Markov Theorem
3.2.3 Multiple Regression From Simple Univariate Regression
3.2.4 Multiple Outputs
3.3 Subset Selection
3.4 Shrinkage Methods
3.4.1 Ridge Regression
3.4.2 The Lasso
3.4.3 Discussion: Subset Selection, Ridge Regression and the Lasso
3.4.4 Least Angle Regression
3.5 Methods Using Derived Input Directions

Chapter 4

Title
4.1 Introduction
4.2 Linear Regression of an Indicator Matrix
4.3 Linear Discriminant Analysis
4.3.1 Regularized Discriminant Analysis
4.3.2 Computations for LDA
4.3.3 Reduced-Rank Linear Discriminant Analysis
4.4 Logistic Regression
4.4.1 Fitting Logistic Regression Models
4.4.2 Example: South African Heart Disease
4.4.3 Quadratic Approximations and Inference
4.4.4 L1 Regularized Logistic Regression

Chapter 11

Title
11.7 Example: ZIP Code Data (WIP)