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Feature Engineering Made Easy
Feature Engineering Made Easy

Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems

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Profile Icon Sinan Ozdemir Profile Icon Susarla
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (11 Ratings)
Paperback Jan 2018 316 pages 1st Edition
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Arrow left icon
Profile Icon Sinan Ozdemir Profile Icon Susarla
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (11 Ratings)
Paperback Jan 2018 316 pages 1st Edition
eBook
zł141.99
Paperback
zł177.99
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eBook
zł141.99
Paperback
zł177.99
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Feature Engineering Made Easy

Feature Understanding – What's in My Dataset?

Finally! We can start to jump into some real data, some real code, and some real results. Specifically, we will be diving deeper into the following ideas:

  • Structured versus unstructured data
  • Quantitative versus qualitative data
  • The four levels of data
  • Exploratory data analysis and data visualizations
  • Descriptive statistics

Each of these topics will give us a better sense of the data given to us, what is present within the dataset, what is not present within the dataset, and some basic notions on how to proceed from there.

If you're familiar with, Principles of Data Science, much of this echoes Chapter 2, Types of Data of that book. That being said, in this chapter, we will specifically look at our data less from a holistic standpoint, and more from a machine-learning standpoint.

...

The structure, or lack thereof, of data

When given a new dataset, it is first important to recognize whether or not your data is structured or unstructured:

  • Structured (organized) data: Data that can be broken down into observations and characteristics. They are generally organized using a tabular method (where rows are observations and columns are characteristics).

  • Unstructured (unorganized) data: Data that exists as a free-flowing entity and does not follow standard organizational hierarchy such as tabularity. Often, unstructured data appears to us as a blob of data, or as a single characteristic (column).

A few examples that highlight the difference between structured and unstructured data are as follows:

  • Data that exists in a raw free-text form, including server logs and tweets, are unstructured

  • Meteorological data, as reported by scientific instruments...

An example of unstructured data – server logs

As an example of unstructured data, we have pulled some sample server logs from a public source and included them in a text document. We can take a glimpse of what this unstructured data looks like, so we can recognize it in the future:

# Import our data manipulation tool, Pandas
import pandas as pd
# Create a pandas DataFrame from some unstructured Server Logs
logs = pd.read_table('../data/server_logs.txt', header=None, names=['Info'])

# header=None, specifies that the first line of data is the first data point, not a column name
# names=['Info] is me setting the column name in our DataFrame for easier access

We created a DataFrame in pandas called logs that hold our server logs. To take a look, let's call the .head() method to look at the first few rows:

# Look at the first 5...

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:

  • Weather measured as temperature in Fahrenheit or Celsius would be quantitative
  • Weather measured as cloudy or sunny would be qualitative
  • The name of a person visiting the White House would be qualitative
  • The amount of blood you donate at a blood drive is quantitative

The first two examples show that we can describe...

The four levels of data

We already know that we can identify data as being either qualitative or quantitative. But, from there, we can go further. The four levels of data are:

  • The nominal level
  • The ordinal level
  • The interval level
  • The ratio level

Each level comes with a varying level of control and mathematical possibilities. It is crucial to know which level data lives on because it will dictate the types of visualizations and operations you are allowed to perform.

The nominal level

The first level of data, the nominal level, has the weakest structure. It consists of data that are purely described by name. Basic examples include blood type (A, O, AB), species of animal, or names of people. These types of data are all qualitative...

Recap of the levels of data

Understanding the various levels of data is necessary to perform feature engineering. When it comes time to build new features, or fix old ones, we must have ways of identifying how to work with every column.

Here is a quick table to summarize what is and isn't possible at every level:

Level of Measurement

Properties

Examples

Descriptive statistics

Graphs

Nominal

Discrete

Orderless

Binary Responses (True or False)

Names of People

Colors of paint

Frequencies/Percentages
Mode

Bar

Pie

Ordinal

Ordered categories

Comparisons

Likert Scales

Grades on an exam

Frequencies

Mode

Median

Percentiles

Bar

Pie

Stem and leaf

Interval

Differences between ordered values have meaning

Deg. C or F

Some Likert Scales (must be specific)

Frequencies

Mode

Median

Mean

Standard Deviation

Bar
Pie
Stem and leaf

Box plot

Histogram...

Summary

Understanding the features that we are working with is step zero of feature engineering. If we cannot understand the data given to us, we will never hope to fix, create, and utilize features in order to create well-performing, machine-learning pipelines. In this chapter, we were able to recognize, and extract the levels of data from our datasets and use that information to create useful and meaningful visuals that shine new lights on our data.

In the next chapter, we will use all of this new-found knowledge of the levels of data to start improving our features, and we will start to use machine-learning to effectively measure the impact of our feature engineering pipelines.

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Key benefits

  • Design, discover, and create dynamic, efficient features for your machine learning application
  • Understand your data in-depth and derive astonishing data insights with the help of this Guide
  • Grasp powerful feature-engineering techniques and build machine learning systems

Description

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.

Who is this book for?

If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.

What you will learn

  • Identify and leverage different feature types
  • Clean features in data to improve predictive power
  • Understand why and how to perform feature selection, and model error analysis
  • Leverage domain knowledge to construct new features
  • Deliver features based on mathematical insights
  • Use machine-learning algorithms to construct features
  • Master feature engineering and optimization
  • Harness feature engineering for real world applications through a structured case study

Product Details

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Publication date : Jan 22, 2018
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781787287600
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Product Details

Publication date : Jan 22, 2018
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781787287600
Category :
Languages :
Tools :

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Table of Contents

9 Chapters
Introduction to Feature Engineering Chevron down icon Chevron up icon
Feature Understanding – What's in My Dataset? Chevron down icon Chevron up icon
Feature Improvement - Cleaning Datasets Chevron down icon Chevron up icon
Feature Construction Chevron down icon Chevron up icon
Feature Selection Chevron down icon Chevron up icon
Feature Transformations Chevron down icon Chevron up icon
Feature Learning Chevron down icon Chevron up icon
Case Studies Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Metodi Todorov Sep 30, 2019
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I really like these kind of books, which explains complex topics - short and clear with simple examples. Thank You.
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Amazon Customer Mar 26, 2018
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This book is step-by-step, easy to follow, and really focused on an applied approach. It's already helped me at work on a current project!
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Temitope Aug 11, 2020
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Amazing book..
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joe hoeller Aug 14, 2018
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Very good book for beginners that want to build a solid foundation, and even a bit beyond that, before moving to more advanced principals.
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Rohan Shamapant Mar 24, 2018
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A really informative read, includes some of the most relevant information pertaining to data learning.
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