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Computer Science
Data Science
Predictive Analytics
1. Foundations of Predictive Analytics
2. Data Foundation and Preparation
3. Regression Modeling
4. Classification Modeling
5. Ensemble Methods
6. Neural Networks and Deep Learning
7. Time Series Analysis and Forecasting
8. Unsupervised Learning
9. Model Evaluation and Validation
10. Model Interpretability and Explainability
11. Model Deployment and Production
12. Business Applications and Use Cases
13. Ethics and Responsible AI
Model Interpretability and Explainability
Interpretability Fundamentals
Global vs Local Interpretability
Model-specific vs Model-agnostic Methods
Interpretability vs Accuracy Tradeoff
Intrinsically Interpretable Models
Linear Models
Coefficient Interpretation
Feature Importance
Decision Trees
Rule Extraction
Path Analysis
Rule-based Models
Decision Rules
Association Rules
Model-agnostic Explanation Methods
Permutation Feature Importance
Feature Shuffling
Importance Ranking
Partial Dependence Plots
Marginal Effects
Interaction Effects
LIME
Local Linear Approximation
Instance-specific Explanations
Perturbation Strategies
SHAP
Shapley Value Theory
Additive Feature Attribution
SHAP Value Calculation
Visualization Techniques
Global Explanation Techniques
Feature Importance Rankings
Model Summaries
Surrogate Models
Global Surrogate
Local Surrogate
Visualization for Interpretability
Feature Effect Plots
Interaction Plots
Decision Boundaries
Model Behavior Visualization
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9. Model Evaluation and Validation
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11. Model Deployment and Production