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Data Interview Question

Identifying Key Features in Opaque Models

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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?