This book is a very nice introduction to statistical learning theory. One of the great aspects of the book is that it is very practical in its approach, focusing much effort into making sure that the reader understands how to actually apply the techniques presented. The book does this by demonstrating their use in the freely available R language. At the end of each chapter are sample R sessions that present the inputs and outputs when running the various techniques discussed in the chapter text on actual data. This is a great approach because it enables the reader to quickly study and experiment with a great number of machine learning using actual R code and data. It is then an easy exercise to modify the R code to work on different data sets if desired. For the applied statistician this is a great help because it cuts the research time down considerably. I'm not the only one who has a very high view of this book. Readers can see what others have said here. To make sure I understood this material as well as possible, as I read the book, I worked all the conceptual and applied exercises at the end of each chapter. Linked to this page are the R scripts I wrote for each chapter. It is my hope that students of machine learning and statistics will find this material helpful. In addition to the R scripts I wrote up solutions to these exercises and put them in book form. Originally these notes and solutions were written in PDF (using the mathematical typesetting language LaTeX). I converted the PDF format to a format I thought more people would find easier to read. You can preview and buy a kindle version of the book here. If you are interested in purchasing the PDF version you can do so for $41.00 (US dollars) (please see the links below). 3/8/2021RPubs - ISL - 8. Tree-Based Methods2.It is mentioned in Section 8.2.3 that boosting using depth-one trees (or stumps) leads to an additive model: that is, amodel of the formExplain why this is the case. You can begin with (8.12) inAlgorithm 8.2. Get answer to your question and much more 1/23.Consider the Gini index, classification error, and entropy in asimple classification setting with two classes. Create a single Upload your study docs or become a Course Hero member to access this document An Introduction to Statistical Learning is a textbook by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Conceptual and applied exercises are provided at the end of each chapter covering supervised learning. This repository contains my solutions to the labs and exercises as Jupyter Notebooks written in Python using:
Perhaps of most interest will be the recreation of some functions from the R language that I couldn't find in the Python ecosystem. These took me some time to reproduce but the implementation details are not essential to the concepts taught in the book so please feel free to reuse. For example, a reproduction of R's To view notebooksLinks to view each notebook below. The code is provided here. Chapter 2 - Statistical Learning: Conceptual Chapter 3 - Linear Regression: Conceptual Chapter 4 - Classification: Conceptual Chapter 5 - Resampling Methods: Conceptual Chapter 6 - Linear Model Selection and Regularization: Labs Chapter 7 - Moving Beyond Linearity: Labs Chapter 8 - Tree-Based Methods: Labs Chapter 9 - Support Vetor Machines: Labs To run notebooksRunning the notebooks enables you to execute the code and play around with any interactive features. To run:
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Introduction to statistical learning solutions chapter 8
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