Sample interview questions: How do you handle issues of model interpretability in support vector machines?
Sample answer:
- Use appropriate kernels: Different kernels can result in different levels of model interpretability. For example, linear kernels are more interpretable than polynomial or Gaussian kernels.
- Regularize the model: Regularization can help to improve model interpretability by reducing the number of features in the model and by penalizing large coefficients.
- Use feature selection: Feature selection can help to identify the most important features in the model, which can make the model more interpretable.
- Use explainable AI (XAI) techniques: XAI techniques can help to explain the predictions of a model and to identify the features that are most important for making those predictions.
- Visualize the model: Visualizing the model can help to understand the decision boundary and to identify any potential problems with th… Read full answer