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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

Product type Paperback

Published in Apr 2018

Publisher Packt

ISBN-13 9781788629898

Length 282 pages

Edition 1st Edition

Languages

Python

Tools

Assemble

Concepts

Machine Learning

Authors (2):

Das

Mert Cakmak

View More author details

Table of Contents (15) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Introduction to AutoML FREE CHAPTER

2. Introduction to Machine Learning Using Python

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

1. Other Books You May Enjoy

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Index

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's set to False:

from sklearn.linear_model import LogisticRegression

log_reg = LogisticRegression(C=10, tol=0.00001)

from sklearn...

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Authors (2)

Das

Das

Sibanjan Das is a Business Analytics and Data Science consultant. He has extensive experience in implementing predictive analytics solutions in Business Systems and IoT. An enthusiastic and passionate professional about technology and innovation, he has the passion for wrangling with data since early days of his career. Sibanjan holds a Masters IT degree with major in Business Analytics from Singapore Management University and holds several industry certifications such as OCA, OCP and CSCMS.

See other products by Das

Mert Cakmak

Mert Cakmak

Umit Mert Cakmak is a data scientist at IBM, where he excels at helping clients solve complex data science problems, from inception to delivery of deployable assets. His research spans multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

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