Machine Learning Interview Questions & Answers(Complex)
Question: Whats the difference between Black-box and white-box models?Ans:
- accurate and ‘black-box’:
Black-box models such as neural networks, gradient boosting models or complicated ensembles often provide great accuracy. The inner workings of these models are harder to understand and they don’t provide an estimate of the importance of each feature on the model predictions, nor is it easy to understand how the different features interact. - weaker and ‘white-box’:
Simpler models such as linear regression and decision trees on the other hand provide less predictive capacity and are not always capable of modelling the inherent complexity of the dataset (i.e. feature interactions). They are however significantly easier to explain and interpret.
Question:What is the difference between Surogate and LIME interpretability Technique?
Ans: Surrogate models are (generally simpler) models that are used to explain a more complex model. Linear models and decision tree models are often used because of their simple interpretation. The surrogate model is created to represent the decision making process of the complex model (the response function) and is a model trained on the input and model predictions, rather than input and targets. Surrogate models provide a layer of global interpretability on top of non-linear and non-monotonic models, but they should not be relied on exclusively.The general idea behind LIME is the same as surrogate models. LIME however does not build a global surrogate model that represents the entire dataset and only builds local surrogate models (linear models) that explain the predictions at local regions.
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Question: Explain LIME interpretability Technique in detail ?
Ans:
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Question: What are the Random Forest Importances? Explain in Detail?
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