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This book is an exploration of machine learning. It focuses on Bagging and Boosting machine learning algorithms, which belong to the category of ensemble learning. The author helps you firstly familiarize yourself with the ensemble method. The various aspects of the decision tree algorithm have been explored in detail. Then the author guides you on how to create and use bagged trees in machine learning. The random forest algorithm has been discussed in depth. The various types of Boosting algorithms have been explored. The author has given practical examples including mathematics and…mehr

Produktbeschreibung
This book is an exploration of machine learning. It focuses on Bagging and Boosting machine learning algorithms, which belong to the category of ensemble learning. The author helps you firstly familiarize yourself with the ensemble method. The various aspects of the decision tree algorithm have been explored in detail. Then the author guides you on how to create and use bagged trees in machine learning. The random forest algorithm has been discussed in depth. The various types of Boosting algorithms have been explored. The author has given practical examples including mathematics and programming. Each of these examples is accompanied by clear explanation. Here is a preview of what you'll learn: * - An Overview of Ensemble Method * - Decision Tree Algorithm * - Bagging * - Random Forest * - Boosting Tags: bagging machine, boosting machine learning, ensemble machine learning, bagging machine learning, random forest machine learning, tree algorithms, machine learning.