MachineLearningWithRandomForestsAndDecis.epub - (EPUB全文下载)

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Machine Learning With Random Forests And Decision Trees

A Visual Guide For Beginners

By Scott Hartshorn

Thank You!
Thank you for getting this book! It contains examples of how the Random Forest Machine Learning algorithm works. It is intended to give you an intuitive understanding of how Random Forests work, so you can apply it to larger problems. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. It also has a moderate amount of math when it dives into the details of some of the different aspects of Random Forests.
This book was mostly written to be programming language agnostic, and focus on how the machine learning works. However there is some code using the Scikit-learn module in Python 2.7
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Your Free Gift
As a way of saying thank you for your purchase, I’m offering this free cheat sheet on Decision Trees that’s exclusive to my readers.
Decision trees form the heart of Random Forests, so it is important to understand how they work. They are also a useful machine learning technique in their own right. This is a 2 page PDF document that I encourage you to print, save, and share.
You can download it by going here

http://www.fairlynerdy.com/decision-trees-cheat-sheet/
A laminated, physical copy of this cheat sheet can also be purchased from Amazon here
http://geni.us/DecisionTrees
(may not be available in all countries)
Random Forest Overview
Random Forests are one type of machine learning algorithm. They are typically used to categorize something based on other data that you have. For instance, you might want to categorize animal types based on their size, weight, and appearance, or you might want to categorize a disease based on a person’s symptoms. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem.
What This Book Is & What It Isn’t
There are two different things to know to be really good at using Random Forests, or any other kind of machine learning. The first is how to set up the code, i.e. how to manipulate the data, how to generate the plots, what software to use etc. This can be considered the nuts and bolts of actually doing the job.
The second thing to know is how the Random Forest is actually working at a conceptual leve ............

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