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Paperback. Condizione: Good. 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 some of 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 Contents: A brief introduction to decision treesChapter 1: Branching - uses a greedy algorithm to build a decision tree from data that can be partitioned on a single attribute.Chapter 2: Multiple Branches - examines several ways to partition data in order to generate multi-level decision trees.Chapter 3: Continuous Attributes - adds the ability to partition 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.…

Condizione: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Paperback. Condizione: Fair. No Jacket. Former library book; Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condizione: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

paperback. Condizione: Good. Paperback. Cover and spine in good condition, corners are bumped. Spine is tight. Pages are clean, no markings, notes or stains. Ships from Friends bookstore to benefit Beaverton (Oregon) library.

Paperback. Condizione: Good. 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 some of 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 Contents: A brief introduction to decision treesChapter 1: Branching - uses a greedy algorithm to build a decision tree from data that can be partitioned on a single attribute.Chapter 2: Multiple Branches - examines several ways to partition data in order to generate multi-level decision trees.Chapter 3: Continuous Attributes - adds the ability to partition 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.…

Soft cover. Condizione: Good. No Jacket. The hinges and gutters are good, The text and photographs are clear. There is some edge wear on the cover. There is a pencil mark on the edges (paper).

Paperback. Condizione: Brand New. 364 pages. 8.90x5.90x1.20 inches. In Stock.

Softcover. Condizione: Very Good+. Condizione sovraccoperta: No Dust Jacket As Issued. First Edition. A crisp, clean, tightly bound softcover copy presenting light creasing to spine from having been previously gently read and presenting light shelf wear only. Signed by the author on the title page.; Signed by Author. …

Condizione: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condizione: Very Good. BOOK IN GOOD CONDITION PAGES ARE CLEAN SIGNED BY THE AUTHOR Sold by the U.K Charity Kisharon Langdon. Offering Opportunities and Support for People within the Autism and Learning Disability Community.

Paperback. Condizione: new. New Copy. Customer Service Guaranteed.

hardcover. Condizione: Very Good. Very Good. Dust Jacket may NOT BE INCLUDED.CDs may be missing. SHIPS FROM MULTIPLE LOCATIONS. book.