Learn The Basics Of Decision Trees A Popular And Powerful Machine Learning Algorithm

One of the most potent algorithms in data science and machine learning is the decision tree algorithm. Data scientists and machine learning engineers utilise it a lot to address business problems and make things easy to understand for your customers. This book will explain decision trees to you and show you how to create one with Python.

Why educate oneself on decision trees? The most popular and often utilised machine learning algorithms are decision trees. Regression and classification difficulties can both be resolved with it.

Decision trees are widely applicable across various industries due to their ease of interpretation. In the book Getting Started with Decision Trees, what would you learn?

  • Overview of Decision Trees
  • Words used in relation to decision trees
  • Various decision tree splitting criteria, such as chi-square, Gini, etc.
  • Python implementation of a decision tree

What You'll Learn

  • Basics of Decision Trees
  • How to Apply Decision Trees to build Machine Learning models
  • Building Decision Tree models in Python
  • How to improve and optimise your decision tree models
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Learn The Basics Of Decision Trees A Popular And Powerful Machine Learning Algorithm

One of the most potent algorithms in data science and machine learning is the decision tree algorithm. Data scientists and machine learning engineers utilise it a lot to address business problems and make things easy to understand for your customers. This book will explain decision trees to you and show you how to create one with Python.

Why educate oneself on decision trees? The most popular and often utilised machine learning algorithms are decision trees. Regression and classification difficulties can both be resolved with it.

Decision trees are widely applicable across various industries due to their ease of interpretation. In the book Getting Started with Decision Trees, what would you learn?

  • Overview of Decision Trees
  • Words used in relation to decision trees
  • Various decision tree splitting criteria, such as chi-square, Gini, etc.
  • Python implementation of a decision tree

What You'll Learn

  • Basics of Decision Trees
  • How to Apply Decision Trees to build Machine Learning models
  • Building Decision Tree models in Python
  • How to improve and optimise your decision tree models
4.99 In Stock
Learn The Basics Of Decision Trees A Popular And Powerful Machine Learning Algorithm

Learn The Basics Of Decision Trees A Popular And Powerful Machine Learning Algorithm

by UBER AUTHOR
Learn The Basics Of Decision Trees A Popular And Powerful Machine Learning Algorithm

Learn The Basics Of Decision Trees A Popular And Powerful Machine Learning Algorithm

by UBER AUTHOR

eBook

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Overview

One of the most potent algorithms in data science and machine learning is the decision tree algorithm. Data scientists and machine learning engineers utilise it a lot to address business problems and make things easy to understand for your customers. This book will explain decision trees to you and show you how to create one with Python.

Why educate oneself on decision trees? The most popular and often utilised machine learning algorithms are decision trees. Regression and classification difficulties can both be resolved with it.

Decision trees are widely applicable across various industries due to their ease of interpretation. In the book Getting Started with Decision Trees, what would you learn?

  • Overview of Decision Trees
  • Words used in relation to decision trees
  • Various decision tree splitting criteria, such as chi-square, Gini, etc.
  • Python implementation of a decision tree

What You'll Learn

  • Basics of Decision Trees
  • How to Apply Decision Trees to build Machine Learning models
  • Building Decision Tree models in Python
  • How to improve and optimise your decision tree models

Product Details

ISBN-13: 9798868970085
Publisher: UBER AUTHOR
Publication date: 11/03/2023
Sold by: Barnes & Noble
Format: eBook
Pages: 103
File size: 887 KB
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