- AN INTRODUCTION TO STATISTICAL LEARNING PYTHON CODE
- AN INTRODUCTION TO STATISTICAL LEARNING PYTHON DOWNLOAD
Linear and Nonlinear Waves by Gerald Beresford Whitham. Numerical Methods for Conservation Laws by Randall J. Heinrich.įinite Volume Methods for Hyperbolic Problems by Randall J. The Finite Element Method: Basic Concepts and Applications by D. Numerical Computation of Internal and External Flows: Volume 1 & 2 by C. Numerical Solution Techniques for Differential Equations
AN INTRODUCTION TO STATISTICAL LEARNING PYTHON DOWNLOAD
The book contains sections with applications in R based on public datasets available for download or which are part of the R-package ISLR. The book is available for download (see link below), but I think this is one of those books that is definitely worth buying. This great book gives a thorough introduction to the field of Statistical/Machine Learning. This is a python wrapper for the Fortran library used in the R package glmnet.Ĭhapter 6 - Linear Model Selection and RegularizationĮxtra: Misclassification rate simulation - SVM and Logistic Regression
AN INTRODUCTION TO STATISTICAL LEARNING PYTHON CODE
Thanks and 6: I included Ridge/Lasso regression code using the new python-glmnet library. The notebooks have been tested with these package versions. Minor updates to the repository due to changes/deprecations in several packages. This repository contains Python code for a selection of tables, figures and LAB sections from the first edition of the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013).įor Bayesian data analysis using PyMC3, take a look at this repository.