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Artificial Intelligence
Machine Learning
Feature Engineering for Machine Learning
1. Introduction to Feature Engineering
2. Foundational Concepts
3. Exploratory Data Analysis for Features
4. Data Cleaning and Preparation
5. Feature Scaling and Normalization
6. Categorical Feature Engineering
7. Feature Creation and Generation
8. Temporal Feature Engineering
9. Text Feature Engineering
10. Geospatial Feature Engineering
11. Feature Selection Methods
12. Dimensionality Reduction
13. Advanced Feature Engineering
14. Evaluation and Validation
15. Implementation and Best Practices
16. Common Pitfalls and Solutions
14.
Evaluation and Validation
14.1.
Feature Quality Metrics
14.1.1.
Information Value
14.1.2.
Weight of Evidence
14.1.3.
Mutual Information
14.1.4.
Feature Stability
14.2.
Model Performance Impact
14.2.1.
Baseline Comparisons
14.2.2.
Ablation Studies
14.2.3.
Feature Contribution Analysis
14.3.
Feature Interpretability
14.3.1.
SHAP Values
14.3.2.
LIME Explanations
14.3.3.
Partial Dependence Plots
14.3.4.
Feature Interaction Plots
14.4.
Cross-Validation Strategies
14.4.1.
K-Fold Cross-Validation
14.4.2.
Stratified Cross-Validation
14.4.3.
Time Series Validation
14.4.4.
Group-Based Validation
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15. Implementation and Best Practices