explaining-machine-learning-models

Explains why ML models make predictions and highlights key features influencing outcomes.

• Analyzes ML model predictions and decisions
• Identifies key features driving model outputs
• Provides transparent, interpretable insights
• Enables understanding of model behavior

• Selects appropriate explanation techniques (e.g., SHAP, LIME)
• Analyzes context and user request
• Generates feature importance and prediction explanations
• Supports debugging and performance analysis
• Facilitates communication with non-technical stakeholders
• Integrates with data preprocessing and visualization tools
• Highlights influential features and interactions
• Ensures model transparency and fairness

1. 1.Analyze the user's request and model data
2. 2.Select the best explanation technique based on model type and need
3. 3.Generate explanations using selected technique
4. 4.Present results clearly with key insights and feature importances