Feature Engineering Made Easy电子书

Title Page

Copyright and Credits

Feature Engineering Made Easy

Packt Upsell

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Contributors

About the authors

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

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Introduction to Feature Engineering

Motivating example – AI-powered communications

Why feature engineering matters

What is feature engineering?

Understanding the basics of data and machine learning

Supervised learning

Unsupervised learning

Unsupervised learning example – marketing segments

Evaluation of machine learning algorithms and feature engineering procedures

Example of feature engineering procedures – can anyone really predict the weather?

Steps to evaluate a feature engineering procedure

Evaluating supervised learning algorithms

Evaluating unsupervised learning algorithms

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

Salary ranges by job classification

The four levels of data

The nominal level

Mathematical operations allowed

The ordinal level

Mathematical operations allowed

The interval level

Mathematical operations allowed

Plotting two columns at the interval level

The ratio level

Mathematical operations allowed

Recap of the levels of data

Summary

Feature Improvement - Cleaning Datasets

Identifying missing values in data

The Pima Indian Diabetes Prediction dataset

The exploratory data analysis (EDA)

Dealing with missing values in a dataset

Removing harmful rows of data

Imputing the missing values in data

Imputing values in a machine learning pipeline

Pipelines in machine learning

Standardization and normalization

Z-score standardization

The min-max scaling method

The row normalization method

Putting it all together

Summary

Feature Construction

Examining our dataset

Imputing categorical features

Custom imputers

Custom category imputer

Custom quantitative imputer

Encoding categorical variables

Encoding at the nominal level

Encoding at the ordinal level

Bucketing continuous features into categories

Creating our pipeline

Extending numerical features

Activity recognition from the Single Chest-Mounted Accelerometer dataset

Polynomial features

Parameters

Exploratory data analysis

Text-specific feature construction

Bag of words representation

CountVectorizer

CountVectorizer parameters

The Tf-idf vectorizer

Using text in machine learning pipelines

Summary

Feature Selection

Achieving better performance in feature engineering

A case study – a credit card defaulting dataset

Creating a baseline machine learning pipeline

The types of feature selection

Statistical-based feature selection

Using Pearson correlation to select features

Feature selection using hypothesis testing

Interpreting the p-value

Ranking the p-value

Model-based feature selection

A brief refresher on natural language processing

Using machine learning to select features

Tree-based model feature selection metrics

Linear models and regularization

A brief introduction to regularization

Linear model coefficients as another feature importance metric

Choosing the right feature selection method

Summary

Feature Transformations

Dimension reduction – feature transformations versus feature selection versus feature construction

Principal Component Analysis

How PCA works

PCA with the Iris dataset – manual example

Creating the covariance matrix of the dataset

Calculating the eigenvalues of the covariance matrix

Keeping the top k eigenvalues (sorted by the descending eigenvalues)

Using the kept eigenvectors to transform new data-points

Scikit-learn's PCA

How centering and scaling data affects PCA

A deeper look into the principal components

Linear Discriminant Analysis

How LDA works

Calculating the mean vectors of each class

Calculating within-class and between-class scatter matrices

Calculating eigenvalues and eigenvectors for SW-1SB

Keeping the top k eigenvectors by ordering them by descending eigenvalues

Using the top eigenvectors to project onto the new space

How to use LDA in scikit-learn

LDA versus PCA – iris dataset

Summary

Feature Learning

Parametric assumptions of data

Non-parametric fallacy

The algorithms of this chapter

Restricted Boltzmann Machines

Not necessarily dimension reduction

The graph of a Restricted Boltzmann Machine

The restriction of a Boltzmann Machine

Reconstructing the data

MNIST dataset

The BernoulliRBM

Extracting PCA components from MNIST

Extracting RBM components from MNIST

Using RBMs in a machine learning pipeline

Using a linear model on raw pixel values

Using a linear model on extracted PCA components

Using a linear model on extracted RBM components

Learning text features – word vectorizations

Word embeddings

Two approaches to word embeddings - Word2vec and GloVe

Word2Vec - another shallow neural network

The gensim package for creating Word2vec embeddings

Application of word embeddings - information retrieval

Summary

Case Studies

Case study 1 - facial recognition

Applications of facial recognition

The data

Some data exploration

Applied facial recognition

Case study 2 - predicting topics of hotel reviews data

Applications of text clustering

Hotel review data

Exploration of the data

The clustering model

SVD versus PCA components

Latent semantic analysis

Summary

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