Jupyter notebooks for the book "The Elements of Statistical Learning".

This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.

Requirements

jupyter
pandas
numpy
matplotlib
scipy
tensorflow 2 - temporarily until I have a lot of free time to implement them from scratch and it is used only in Chapter 11.

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Update 3.2-linear-regression-models-and-least-squares.ipynb

Since X is a N x (p+1) matrix (with the additional columns of 1s for the intercept \beta_0), the trace of H is trace(X(X^TX)^{-1}X^T) = trace(I_{p+1}) = (p+1). That's how you get that \widehat{\sigma^2}, conditional on X, is an unbiased estimator of \sigma^2.

By law of expectations, this also implies \widehat{\sigma^2} is an unconditionally unbiased estimator of \sigma^2.

opened by silpian 1

Jupyter notebooks for the book "The Elements of Statistical Learning".

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Overview

Jupyter notebooks for the book "The Elements of Statistical Learning".

Requirements

Table of Contents

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Update 3.2-linear-regression-models-and-least-squares.ipynb

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