Description: Tree-based Machine Learning Algorithms : Decision Trees, Random Forests, and Boosting, Paperback by Sheppard, Clinton, ISBN 1975860977, ISBN-13 9781975860974, Brand New, Free shipping in the US 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 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 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 of Contents:A 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 2: Multiple Branches - examines several ways to split data in order to generate multi-level decision 3: Continuous Attributes - adds the ability to split numeric attributes using 4: Pruning - explore ways of reducing the amount of error encoded in the 5: Random Forests - introduces ensemble learning and feature 6: Regression Trees - investigates numeric predictions, like age, price, and miles per 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.
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Book Title: Tree-based Machine Learning Algorithms : Decision Trees, Random F
Number of Pages: 152 Pages
Publication Name: Tree-Based Machine Learning Algorithms : Decision Trees, Random Forests, and Boosting
Language: English
Publisher: CreateSpace
Item Height: 0.3 in
Publication Year: 2017
Subject: Programming / Algorithms
Type: Textbook
Item Weight: 10.1 Oz
Item Length: 9 in
Subject Area: Computers
Author: Clinton Sheppard
Item Width: 6 in
Format: Trade Paperback
