UsefulLinks
Computer Science
Data Science
Data Mining and Knowledge Discovery
1. Introduction to Data Mining and Knowledge Discovery
2. Data Types and Sources
3. Data Preprocessing Fundamentals
4. Classification Methods
5. Regression Analysis
6. Clustering Analysis
7. Association Rule Mining
8. Advanced Mining Techniques
9. Model Evaluation and Validation
10. Model Interpretation and Explainability
11. Deployment and Production Systems
12. Ethics, Privacy, and Security
10.
Model Interpretation and Explainability
10.1.
Interpretability Concepts
10.1.1.
Global vs Local Interpretability
10.1.2.
Model-Agnostic vs Model-Specific Methods
10.1.3.
Intrinsic vs Post-Hoc Interpretability
10.2.
Feature Importance Analysis
10.2.1.
Permutation Importance
10.2.2.
Feature Contribution Methods
10.2.3.
Partial Dependence Plots
10.2.4.
SHAP Values
10.3.
Local Explanation Methods
10.3.1.
LIME Algorithm
10.3.2.
Local Surrogate Models
10.3.3.
Counterfactual Explanations
10.4.
Global Explanation Methods
10.4.1.
Model Distillation
10.4.2.
Rule Extraction
10.4.3.
Global Surrogate Models
10.5.
Visualization Techniques
10.5.1.
Decision Tree Visualization
10.5.2.
Feature Space Visualization
10.5.3.
Cluster Visualization
10.5.4.
Association Rule Visualization
10.5.5.
Network Visualization
10.6.
Knowledge Representation
10.6.1.
Rule-Based Representations
10.6.2.
Semantic Networks
10.6.3.
Ontologies
10.6.4.
Report Generation
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11. Deployment and Production Systems