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Feature Engineering Made Easy

You're reading from Feature Engineering Made Easy Identify unique features from your dataset in order to build powerful machine learning systems

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781787287600

Length 316 pages

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Machine Learning

Authors (2):

Sinan Ozdemir

Susarla

View More author details

Table of Contents (14) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Introduction to Feature Engineering FREE CHAPTER

2. Feature Understanding – What's in My Dataset?

3. Feature Improvement - Cleaning Datasets

4. Feature Construction

5. Feature Selection

6. Feature Transformations

7. Feature Learning

8. Case Studies

1. Other Books You May Enjoy

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Summary

In this chapter, we learned a great deal about methodologies for selecting subsets of features in order to increase the performance of our machine learning pipelines in both a predictive capacity as well in-time-complexity.

The dataset that we chose had a relatively low number of features. If selecting, however, from a very large set of features (over a hundred), then the methods in this chapter will likely start to become entirely too cumbersome. We saw that in this chapter, when attempting to optimize a CountVectorizer pipeline, the time it would take to run a univariate test on every feature is not only astronomical; we would run a greater risk of experiencing multicollinearity in our features by sheer coincidence.

In the next chapter, we will introduce purely mathematical transformations that we may apply to our data matrices in order to alleviate the trouble of working with vast quantities of features, or even a few highly uninterpretable features. We will begin to work with...

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Authors (2)

Sinan Ozdemir

Sinan Ozdemir

Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

See other products by Sinan Ozdemir

Susarla

Susarla

Divya Susarla is an experienced leader in data methods, implementing and applying tactics across a range of industries and fields including investment management, social enterprise consulting, and wine marketing. She trained in data by way of specializing in Economics and Political Science at University of California, Irvine, cultivating a passion for teaching by developing an analytically based, international affairs curriculum for students through the Global Connect program. Divya is currently focused on natural language processing and generation techniques at Kylie.ai, a startup helping clients automate their customer support conversations. When she is not busy working on building Kylie.ai and writing educational content, she spends her time traveling across the globe and experimenting with new recipes at her home in Berkeley, CA.

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