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Hands-On Automated Machine Learning

You're reading from   Hands-On Automated Machine Learning A beginner's guide to building automated machine learning systems using AutoML and Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788629898
Length 282 pages
Edition 1st Edition
Languages
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Authors (2):
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Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
Sibanjan Das Sibanjan Das
Author Profile Icon Sibanjan Das
Sibanjan Das
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to AutoML 2. Introduction to Machine Learning Using Python FREE CHAPTER 3. Data Preprocessing 4. Automated Algorithm Selection 5. Hyperparameter Optimization 6. Creating AutoML Pipelines 7. Dive into Deep Learning 8. Critical Aspects of ML and Data Science Projects 9. Other Books You May Enjoy

Decision trees

Decision trees are extensively-used classifiers in the ML world for their transparency on representing the rules that drive a classification/prediction. Let us ask the triple W questions to this algorithm to know more about it.

What are decision trees?

Decision trees are arranged in a hierarchical tree-like structure and are easy to explain and interpret. They are not susceptive to outliers. The process of creating a decision tree is a recursive partitioning method where it splits the training data into various groups with an objective to find homogeneous pure subgroups, that is, data with only one class.

Outliers are values that lie far away from other data points and distort the data distribution.
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You have been reading a chapter from
Hands-On Automated Machine Learning
Published in: Apr 2018
Publisher: Packt
ISBN-13: 9781788629898
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