Introduction to Feature Engineering
Chapter 1: The Role of Features in Machine Learning
Revisiting the Machine Learning Workflow
What Constitutes a Feature?
Impact of Feature Quality on Model Performance
Common Data Types and Their Challenges
Overview of Feature Engineering Tasks
Quiz for Chapter 1
Chapter 2: Handling Missing Data
Identifying Missing Values
Mechanisms of Missing Data (MCAR, MAR, MNAR)
Simple Imputation Strategies: Mean, Median, Mode
Creating Missing Value Indicators
Multivariate Imputation: KNN Imputer
Multivariate Imputation: Iterative Imputer
Comparing Imputation Methods
Hands-on Practical: Imputing Missing Data
Quiz for Chapter 2
Chapter 3: Encoding Categorical Features
Challenges with Categorical Data
Nominal vs. Ordinal Categories
One-Hot Encoding for Nominal Features
Ordinal Encoding for Ordered Features
Handling High Cardinality Features
Target Encoding (Mean Encoding)
Binary Encoding
Hashing Encoder
Comparing Encoding Methods
Hands-on Practical: Applying Encoding Techniques
Quiz for Chapter 3
Chapter 4: Feature Scaling and Transformation
The Need for Feature Scaling
Standardization (Z-score Scaling)
Normalization (Min-Max Scaling)
Scaling for Outliers
Log Transformation for Skewed Data
Box-Cox Transformation
Yeo-Johnson Transformation
Quantile Transformation
Choosing the Right Scaling/Transformation Method
Hands-on Practical: Scaling and Transforming Features
Quiz for Chapter 4
Chapter 5: Feature Creation
Motivation for Creating New Features
Interaction Features
Polynomial Features
Feature Creation from Date/Time Data
Binning Numerical Features
Domain-Specific Feature Engineering
Automated Feature Creation (Introduction)
Hands-on Practical: Engineering New Features
Quiz for Chapter 5
Chapter 6: Feature Selection
Importance of Feature Selection
Filter Methods Overview
Filter Methods: Variance Threshold
Filter Methods: Univariate Statistical Tests (ANOVA F-value, Chi-Squared)
Filter Methods: Correlation Analysis
Wrapper Methods Overview
Wrapper Methods: Recursive Feature Elimination (RFE)
Wrapper Methods: Sequential Feature Selection (SFS)
Embedded Methods Overview
Embedded Methods: Regularization (Lasso L1)
Embedded Methods: Tree-Based Feature Importance
Hands-on Practical: Selecting Features
Quiz for Chapter 6
Creating Missing Value Indicators
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- Feature Engineering for Machine Learning, Alice Zheng, Amanda Casari, 2018 (O'Reilly Media) - This book provides practical strategies for creating effective features, including methods for handling missing data and the use of indicator variables.
- Imputation of missing values, scikit-learn developers, 2023 - Official documentation for the
sklearn.imputemodule, describing various imputation strategies and theadd_indicatorparameter. - Applied Predictive Modeling, Max Kuhn and Kjell Johnson, 2013 (Springer) DOI: 10.1007/978-1-4614-6849-3 - This book offers practical guidance on building predictive models, with sections on data preprocessing and handling missing values.
- A Review on Missing Data Imputation in Machine Learning, Md. Imran Hossain, Md. Asraful Islam, Md. Abdul Mukit, Sheikh Abujar, and Mohammad Shidujaman, 2021 SN Computer Science, Vol. 2 (Springer) DOI: 10.1007/s42979-021-00762-7 - This review article surveys various missing data imputation techniques, offering insights into scenarios where indicator variables are beneficial.