Identifying Key Features in Opaque Models

Requirements Clarification & Assessment

1. Understanding the Model Type:

   • Is the model a black-box or interpretable model? This determines the complexity of extracting feature importance.
   • Examples of black-box models include deep neural networks and ensemble methods like random forests.

2. Objective of Feature Importance:

   • Is the goal to enhance interpretability, improve model performance, or comply with regulatory requirements?
   • Understanding the objective helps in selecting the appropriate method for feature importance.

3. Dataset Characteristics:

   • Size of the dataset: Large datasets might require more computational resources.
   • Nature of features: Are the features continuous, categorical, or a mix?

4. Performance Metrics:

   • What metrics are used to evaluate model performance? (e.g., accuracy, RMSE, F1-score)
   • Knowing the metrics helps in assessing the impact of feature importance methods.

5. Computational Resources:

   • Availability of computational resources may limit the choice of methods, especially for large datasets.

6. Regulatory or Ethical Considerations:

   • Are there any legal or ethical considerations that necessitate transparency in model predictions?