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
Comparing Encoding Methods
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- Preprocessing data, scikit-learn developers, 2023 - Provides implementation details and examples for various encoders, including One-Hot and Ordinal Encoding.
- Feature Engineering for Machine Learning: Principles and Techniques for Building Robust Models, Alice Zheng and Amanda Casari, 2018 (O'Reilly Media) - Offers comprehensive guidance on categorical feature encoding strategies and their practical application.
- A Comprehensive Study of Categorical Feature Encoding Methods for Machine Learning, Lucky Dhankar, Megha Dhankar, Sarika Jain, and Pankaj Kumar Sharma, 2019 International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8 DOI: 10.35940/ijitee.J8528.0881019 - Compares various categorical encoding techniques based on their impact on machine learning model performance.
- Feature Hashing for Large-Scale Multitask Learning, Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg, 2009 Proceedings of the 26th International Conference on Machine Learning (International Machine Learning Society (IMLS)) DOI: 10.48550/arXiv.0902.2206 - Introduces the feature hashing technique, a method relevant for high-dimensional data and streaming.