• Summary
  • Foreward
  • Before we start
  • Section I: Introduction
  • 1: Introduction
    • 1.1: Terminology
    • 1.2: Process of Training a Machine Learning Model
    • 1.3: Preventing Overfitting
    • 1.4: Code Conventions
    • 1.5: Datasets Used
    • 1.6: References
  • Section II: Feature Engineering
  • 2: Domain Specific Feature Engineering
    • 2.1: Introduction
    • 2.2: Domain-Specific Feature Engineering
    • 2.3: References
  • 3: EDA Feature Engineering
    • 3.1: Introduction
    • 3.2: Car Sales
    • 3.3: Coupon Recommendation
    • 3.4: Conclusion
  • 4: Higher Order Feature Engineering
    • 4.1: Engineering Categorical Features
    • 4.2: Engineering Ordinal Features
    • 4.3: Engineering Numerical Features
    • 4.4: Conclusion
  • 5: Interaction Effect Feature Engineering
    • 5.1: Interaction Plot
    • 5.2: SHAP
    • 5.3: Putting Everything Together
    • 5.4: Conclusion
    • 5.5: References
  • Section III: Feature Selection
  • 6: Fundamentals of Feature Selection
    • 6.1: Introduction
    • 6.2: Different Feature Selection Methods
    • 6.3: Filter Method
    • 6.4: Wrapper Method
    • 6.5: Putting Everything Together
    • 6.6: Conclusion
  • 7: Feature Selection Concerning Modeling Techniques
    • 7.1: Lasso, Ridge, and ElasticNet
    • 7.2: Feature Importance of Tree Models
    • 7.3: Boruta
    • 7.4: Using Tree-Based Feature Importance for Linear Model
    • 7.5: Using Linear Model Feature Importance for Tree Models
    • 7.6: Linear Regression
    • 7.7: SVM
    • 7.8: PCA
    • 7.9: Putting Everything Together
    • 7.10: Conclusion
  • 8: Feature Selection Using Metaheuristic Algorithms
    • 8.1: Exhaustive Feature Selection
    • 8.2: Genetic Algorithm
    • 8.3: Simulated Annealing
    • 8.4: Ant Colony Optimization
    • 8.5: Particle Swarm Optimization
    • 8.6: Putting Everything Together
    • 8.7: Conclusion
    • 8.8: References
  • Section IV: Model Explanation
  • 9: Explaining Model and Model Predictions to Layman
    • 9.1: Introduction
    • 9.2: Explainable models
    • 9.3: Explanation Techniques
    • 9.4: Putting Everything Together
    • 9.5: Conclusion
    • 9.6: References
  • Section V: Special Chapters
  • 10: Feature Engineering & Selection for Text Classification
    • 10.1: Introduction
    • 10.2: Feature Construction
    • 10.3: Feature Selection
    • 10.4: Feature Extraction
    • 10.5: Feature Reduction
    • 10.6: Conclusion
    • 10.7: References
  • 11: Things That Can Give Additional Improvement
    • 11.1: Introduction
    • 11.2: Hyperparameter Tuning
    • 11.3: Ensemble Learning
    • 11.4: Signal Processing
    • 11.5: Conclusion
    • 11.6: References

Feature Engineering & Selection for Explainable Models

 

A Second Course for Data Scientists

Md Azimul Haque

 

You can buy the full version of the book from Amazon. This web version of the book has content only for chapter-1 & chapter-11.

 

You can cite my book as: Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists (Revised Edition), Md Azimul Haque, 2024

 

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While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

 

© Copyright 2022 Md Azimul Haque. All Rights Reserved.