Book Overview & Buying
Table Of Contents

Feature Engineering Made Easy

By : Sinan Ozdemir, Divya Susarla, Michael Smith

4.4 (11)

Buy this Book

Feature Engineering Made Easy

4.4 (11)

By: Sinan Ozdemir, Divya Susarla, Michael Smith

Buy this Book

Overview of this book

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Introduction to Feature Engineering

Motivating example – AI-powered communications

Why feature engineering matters

What is feature engineering?

Evaluation of machine learning algorithms and feature engineering procedures

Feature understanding – what’s in my dataset?

Feature improvement – cleaning datasets

Feature selection – say no to bad attributes

Feature construction – can we build it?

Feature transformation – enter math-man

Feature learning – using AI to better our AI

Summary

Feature Understanding – What's in My Dataset?

The structure, or lack thereof, of data

An example of unstructured data – server logs

Quantitative versus qualitative data

The four levels of data

Recap of the levels of data

Summary

Feature Improvement - Cleaning Datasets

Identifying missing values in data

Dealing with missing values in a dataset

Standardization and normalization

Summary

Feature Construction

Examining our dataset

Imputing categorical features

Encoding categorical variables

Extending numerical features

Text-specific feature construction

Summary

Feature Selection

Achieving better performance in feature engineering

Creating a baseline machine learning pipeline

The types of feature selection

Choosing the right feature selection method

Summary

Feature Transformations

Dimension reduction – feature transformations versus feature selection versus feature construction

Principal Component Analysis

Scikit-learn's PCA

How centering and scaling data affects PCA

A deeper look into the principal components

Linear Discriminant Analysis

LDA versus PCA – iris dataset

Summary

Feature Learning

Parametric assumptions of data

Restricted Boltzmann Machines

The BernoulliRBM

Extracting RBM components from MNIST

Using RBMs in a machine learning pipeline

Learning text features – word vectorizations

Summary

Case Studies

Case study 1 - facial recognition

Case study 2 - predicting topics of hotel reviews data

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Quantitative versus qualitative data

To accomplish our diagnoses of the various types of data, we will begin with the highest order of separation. When dealing with structured, tabular data (which we usually will be doing), the first question we generally ask ourselves is whether the values are of a numeric or categorical nature.

Quantitative data are data that are numerical in nature. They should be measuring the quantity of something.

Qualitative data are data that are categorical in nature. They should be describing the quality of something.

Basic examples: