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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

Warm start

In terms of Automated ML (AutoML) pipelines, hyperparameter search space can grow really quickly and an exhaustive search becomes impracticable with limited time and finite resources. You need smarter ways to perform this task, especially if you have a large dataset with a complex model working on it. If you find yourself in this kind of situation, a GridSeachCV instances exhaustive search won't be feasible, or random parameter draws of RandomizedSearchCV might not give you the best results given limited time.

The basic idea of warm start is to use the information gained from previous training runs to identify smarter starting points for the next training run.

For example, LogisticRegression has a warm_start parameter, which is set to False by default. The following example shows you the training time the first time, and after the parameter update when it&apos...

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