A perfect guide to speed up the predicting power of machine learning algorithms
About This Book
- 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
Who This Book Is 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
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.
Style and approach
This step-by-step guide with use cases, examples, and illustrations will help you master the concepts of feature engineering.
Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
Table of contents
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
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
Feature Improvement - Cleaning Datasets
- Identifying missing values in data
- Dealing with missing values in a dataset
- Standardization and normalization
- Examining our dataset
- Imputing categorical features
- Encoding categorical variables
- Extending numerical features
- Text-specific feature construction
- Achieving better performance in feature engineering
- Creating a baseline machine learning pipeline
The types of feature selection
- Statistical-based feature selection
- Model-based feature selection
- Choosing the right feature selection method
- 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
- How LDA works
- How to use LDA in scikit-learn
- LDA versus PCA – iris dataset
- 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
- Case study 1 - facial recognition
- Case study 2 - predicting topics of hotel reviews data
- Other Books You May Enjoy
- Title: Feature Engineering Made Easy
- Release date: January 2018
- Publisher(s): Packt Publishing
- ISBN: 9781787287600
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