ModelParameters

Extracts and displays model parameters in a structured format for transparency and reproducibility.

Purpose

The Model Parameters test is designed to provide transparency into model configuration and ensure reproducibility of machine learning models. It accomplishes this by extracting and presenting all relevant parameters that define the model’s behavior, making it easier to audit, validate, and reproduce model training.

Test Mechanism

The test leverages scikit-learn’s API convention of get_params() to extract model parameters. It produces a structured DataFrame containing parameter names and their corresponding values. For models that follow scikit-learn’s API (including XGBoost, RandomForest, and other estimators), all parameters are automatically extracted and displayed.

Signs of High Risk

• Missing crucial parameters that should be explicitly set
• Extreme parameter values that could indicate overfitting (e.g., unlimited tree depth)
• Inconsistent parameters across different versions of the same model type
• Parameter combinations known to cause instability or poor performance
• Default values used for critical parameters that should be tuned

Strengths

• Universal compatibility with scikit-learn API-compliant models
• Ensures transparency in model configuration
• Facilitates model reproducibility and version control
• Enables systematic parameter auditing
• Supports both classification and regression models
• Helps identify potential configuration issues

Limitations

• Only works with models implementing scikit-learn’s get_params() method
• Cannot capture dynamic parameters set during model training
• Does not validate parameter values for model-specific appropriateness
• Parameter meanings and impacts may vary across different model types
• Cannot detect indirect parameter interactions or their effects on model performance