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
Embedded Methods Overview
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- Regression Shrinkage and Selection Via the Lasso, Robert Tibshirani, 1996 Journal of the Royal Statistical Society. Series B (Methodological), Vol. 58 (Royal Statistical Society) DOI: 10.1111/j.2517-6161.1996.tb02080.x - Original paper introducing the Lasso, a foundational regularization technique widely used for embedded feature selection.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - Authoritative textbook with detailed chapters on regularization methods (including Lasso) and tree-based models, providing statistical context for embedded feature selection.
- Feature importance with ensembles, The scikit-learn developers, 2024 (scikit-learn project) - Official scikit-learn documentation illustrating how ensemble methods like Random Forests compute and use feature importances for selection, a common embedded technique.
- A survey on feature selection methods, Jian Cai, Jiuyong Luo, Shuxin Wang, and Siheng Meng, 2018 Journal of Computer Science and Technology, Vol. 33 (Springer Science and Business Media LLC) DOI: 10.1007/s11390-018-1807-7 - A recent review paper offering a broad overview of feature selection techniques, including a discussion of embedded methods and their role in machine learning.