On 6/4/19 8:44 PM, C W wrote:
Thank you all for the replies.
I agree that prediction accuracy is great for evaluating black-box ML
models. Especially advanced models like neural networks, or
not-so-black models like LASSO, because they are NP-hard to solve.
Linear regression is not a black-box. I view prediction accuracy as an
overkill on interpretable models. Especially when you can use
R-squared, coefficient significance, etc.
Prediction accuracy also does not tell you which feature is important.
What do you guys think? Thank you!
Did you read the paper that I sent? ;)
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