How do you ensure the interpretability and explainability of machine learning models used in your research?

Sample interview questions: How do you ensure the interpretability and explainability of machine learning models used in your research?

Sample answer:

1. Use interpretable machine learning models:

   • Choose interpretable ML models, such as linear regression, decision trees, and naive Bayes, which allow for easy understanding of the model’s predictions.
   • Utilize feature importance techniques to identify the most influential features in the model’s predictions.

2. Visualize model predictions:

   • Create plots and graphs to visualize the model’s predictions and relationships between features.
   • Use tools like SHAP (SHapley Additive Explanations) to visualize the impact of individual features on model predictions.

3. Use natural language explanations:

   • Generate natural language explanations (NLEs) to explain the model’s predictions in plain language.
   • Integrate NLEs into your research papers, presentations, and discussions to enhance the interpretability of your findings.

4. Perform sensitivity analysis:

   • Conduct sensitivity analysis to assess how changes in input features affect the model’s predictions.
   • Use sensitivity analysis results to identify potential vulnerabilities or biases in the model.

5. Conduct feature engineering:

   • Perform feature engineering to create more informative and interpretable features.
   • Use domain knowledge to select and transform features that are physically meaningful and relevant to the problem being studied.

6. Regularize or prune the model:

   • Apply regula… Read full answer

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