Advanced Performance Analysis and Model Interpretability

1. Advanced Performance Analysis

1.1 Statistical Analysis Methods

• Hypothesis Testing

Statistical methods to evaluate model performance claims and comparisons.

Techniques:

• Statistical Tests

• McNemar’s test
• Wilcoxon signed-rank
• Student’s t-test
• ANOVA

• Confidence Intervals

• Bootstrap estimates
• Cross-validation intervals
• Prediction intervals
• Error bounds

• Effect Size Analysis

• Cohen’s d
• Odds ratio
• Risk ratio
• Area under curve differences

• Error Analysis

Components:

• Error Decomposition

• Bias analysis
• Variance analysis
• Irreducible error
• Model complexity impact

• Error Distribution

• Error patterns
• Outlier impact
• Residual analysis
• Heteroscedasticity

• Failure Mode Analysis

• Error categorization
• Root cause analysis
• Systematic errors
• Edge cases

1.2 Advanced Metrics

• Specialized Performance Metrics

• Ranking Metrics

• Normalized DCG
• Mean reciprocal rank
• Precision at k
• Average precision

• Probabilistic Metrics

• Log loss
• Brier score
• Calibration metrics
• Proper scoring rules

• Custom Metrics

• Business-specific KPIs
• Domain-specific measures
• Cost-sensitive metrics
• Time-weighted metrics

• Multi-Objective Evaluation

Components:

• Trade-off Analysis

• Pareto efficiency
• Multi-criteria optimization
• Weighted combinations
• Constraint satisfaction

• Fairness Metrics

• Demographic parity
• Equal opportunity
• Disparate impact
• Individual fairness

2. Model Interpretability

2.1 Global Interpretability Methods

• Feature Importance

Techniques:

• Permutation Importance

• Random shuffling
• Feature ranking
• Stability analysis
• Interaction effects

• SHAP (SHapley Additive exPlanations)

• Game theory approach
• Feature attribution
• Global importance
• Interaction values

• Model-Specific Methods

• Random forest importance
• Linear model coefficients
• Neural network weights
• Decision tree splits

• Partial Dependence

Showing how features affect predictions while accounting for other features.

Components:

• Partial Dependence Plots

• Feature effects
• Interaction visualization
• Marginal effects
• Non-linear relationships

• ICE (Individual Conditional Expectation)

• Individual predictions
• Feature impacts
• Local behavior
• Instance analysis

2.2 Local Interpretability Methods

• LIME (Local Interpretable Model-agnostic Explanations)

Explaining individual predictions by approximating the model locally.

Characteristics:

• Local Approximation

• Surrogate models
• Local fidelity
• Interpretable features
• Instance explanation

• Applications

• Text classification
• Image recognition
• Tabular data
• Model debugging

• Limitations

• Stability issues
• Feature selection
• Kernel choice
• Sampling strategy

• Counterfactual Explanations

Generating alternative scenarios that would change the model’s prediction.

Components:

• Generation Methods

• Optimization-based
• Genetic algorithms
• Gradient-based
• Rule-based

• Properties

• Minimal changes
• Feasibility
• Diversity
• Actionability

• Applications

• Decision support
• Customer feedback
• Regulatory compliance
• Model improvement

2.3 Visualization Techniques

• Decision Boundaries

Components:

• Visualization Methods

• 2D projections
• Decision surfaces
• Boundary plots
• Region analysis

• Interactive Tools

• Parameter exploration
• Feature interaction
• Instance inspection
• Threshold adjustment

• Attribution Visualization

Techniques:

• Saliency Maps

• Gradient-based
• Attention maps
• Feature attribution
• Class activation

• Feature Interaction

• Dependency graphs
• Interaction strength
• Network visualization
• Hierarchy plots

2.4 Model-Specific Interpretability

• Tree-Based Models

Methods:

• Tree Visualization

• Path highlighting
• Node importance
• Split criteria
• Leaf analysis

• Rule Extraction

• Decision paths
• Rule sets
• Condition importance
• Coverage analysis
• Neural Networks

Techniques:

• Layer Visualization

• Activation patterns
• Filter visualization
• Feature maps
• Attention weights

• Network Analysis

• Weight analysis
• Neuron behavior
• Path importance
• Architecture impact

For more information on various data science algorithms, please visit Data Science Algorithms.