Read Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

Clinton Sheppard

4.9/5 (13772 ratings)

Description:Get a hands-on introduction to building and using decision trees and random forests. Tree-based machine learning algorithms are used to categorize data based on known outcomes in order to facilitate predicting outcomes in new situations.You will learn not only how to use decision trees and random forests for classification and regression, and their respective limitations, but also how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you.This book uses Python, an easy to read programming language, as a medium for teaching you how these algorithms work, but it isn't about teaching you Python, or about using pre-built machine learning libraries specific to Python. It is about teaching you how some of the algorithms inside those kinds of libraries work and why we might use them, and gives you hands-on experience that you can take back to your favorite programming environment.Table of ContentsA brief introduction to decision treesChapter 1: Branching - uses a greedy algorithm to build a decision tree from data that can be split on a single attribute.Chapter 2: Multiple Branches - examines several ways to split data in order to generate multi-level decision trees.Chapter 3: Continuous Attributes - adds the ability to split numeric attributes using greater-than.Chapter 4: Pruning - explore ways of reducing the amount of error encoded in the tree.Chapter 5: Random Forests - introduces ensemble learning and feature engineering.Chapter 6: Regression Trees - investigates numeric predictions, like age, price, and miles per gallon.Chapter 7: Boosting - adjusts the voting power of the randomly selected decision trees in the random forest in order to improve its ability to predict outcomes.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting. To get started finding Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 100

Format: PDF, EPUB & Kindle Edition

Publisher: —

Release: 2017

ISBN

Description: Get a hands-on introduction to building and using decision trees and random forests. Tree-based machine learning algorithms are used to categorize data based on known outcomes in order to facilitate predicting outcomes in new situations.You will learn not only how to use decision trees and random forests for classification and regression, and their respective limitations, but also how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you.This book uses Python, an easy to read programming language, as a medium for teaching you how these algorithms work, but it isn't about teaching you Python, or about using pre-built machine learning libraries specific to Python. It is about teaching you how some of the algorithms inside those kinds of libraries work and why we might use them, and gives you hands-on experience that you can take back to your favorite programming environment.Table of ContentsA brief introduction to decision treesChapter 1: Branching - uses a greedy algorithm to build a decision tree from data that can be split on a single attribute.Chapter 2: Multiple Branches - examines several ways to split data in order to generate multi-level decision trees.Chapter 3: Continuous Attributes - adds the ability to split numeric attributes using greater-than.Chapter 4: Pruning - explore ways of reducing the amount of error encoded in the tree.Chapter 5: Random Forests - introduces ensemble learning and feature engineering.Chapter 6: Regression Trees - investigates numeric predictions, like age, price, and miles per gallon.Chapter 7: Boosting - adjusts the voting power of the randomly selected decision trees in the random forest in order to improve its ability to predict outcomes.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting. To get started finding Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

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