Timeline for How to tune parameters through cross-validation without grid search?
Current License: CC BY-SA 3.0
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| May 27, 2015 at 4:24 | comment | added | Matthew Drury |
For 1), GridSearchCV and LassoCV both use a grid search. For 2), I don't quite follow what you're proposing, could you please explain what you mean by "get a result" in more detail? Is the result the prediction itself or the estimated error?
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| May 27, 2015 at 4:05 | comment | added | DukeJun | 1) Like in scikit-learn, there are CV methods with and without grid search, e.g. grid_search.GridSearchCV() and LassoCV(). 2)Let' say 2-fold CV. In my mind, the evaluation through CV should be training the model on the first half and find a set of good parameters. Then use the trained model on the rest half and get a result. Do the same thing on the second half and get another result. Use the mean of the two results as the performance of this model and compare different models by the same way | |
| May 26, 2015 at 3:48 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
light editing & formatting
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| May 26, 2015 at 3:14 | answer | added | Matthew Drury | timeline score: 4 | |
| May 26, 2015 at 3:02 | comment | added | Matthew Drury | A couple clarifications before someone can answer: Can you clarify what you are asking in 1), is it "what about the performance of the parameters outside of the grid? For 2), how are you planning on evaluation model performance in your proposal? | |
| May 26, 2015 at 2:53 | review | First posts | |||
| May 26, 2015 at 3:48 | |||||
| May 26, 2015 at 2:52 | history | asked | DukeJun | CC BY-SA 3.0 |