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Results tagged with hyperparameter
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user 4598
A parameter that is not strictly for the statistical model (or data generating process), but a parameter for the statistical method. It could be a parameter for: a family of prior distributions, smoothing, a penalty in regularization methods, or an optimization algorithm.
25
votes
How to build the final model and tune probability threshold after nested cross-validation?
That is, the hyperparameter tuning is part of "the method for building the model". … Though as you note other people have a different view (without hyperparameter tuning). …
- 39.6k
10
votes
How to get hyper parameters in nested cross validation?
An inner e.g. cross validation takes care of the hyperparameter optimization. … The crucial step/assumption here to solve the dilemma of whose hyperparameter set should be chosen is to require the optimization to be stable. …
- 39.6k
8
votes
How bad is hyperparameter tuning outside cross-validation?
You can keep the risk low by evaluating only very few models for fixing the regularization hyperparameter plus going for a low complexity within the plausible choice. …
- 39.6k
8
votes
Accepted
Overfitting during model selection - AutoML vs Grid search
First of all, it is crucial to realize that the overfitting described in the Cawley paper arises from selecting the model with apparently best performance where determining performance is subject to …
- 39.6k
4
votes
Grid Search for hyperparameter and feature selection
The most important downside for searching along single parameters instead of optimizing them all together is that you ignore interactions. It is quite common that e.g. more than one parameter influenc …
- 39.6k
3
votes
How to select penalty parameter after cross validation?
Choose $λ=λ^∗$ to minimize the average OOS mean square error.
This strategy assumes you have enough independent test cases so the error on your OOS estimate is negligible.
You are right: if the …
- 39.6k
3
votes
Accepted
Preprocessing+hyperparameter selection: nested or nested nested cross validation?
Yes it holds for preprocessing as well.
- 39.6k
3
votes
Accepted
Repeated Nested Cross validation
Yes, you can repeat the cross validations (both or any of them, as necessary).
Repeated cross validation improves the estimate of generalization error in a specific way: it allows you to easily measu …
- 39.6k
2
votes
Accepted
Using cross-validation both for feature selection and hyperparameters optimization
However, which features to use is just a hyperparameter of your modeling process - you can include it with the optimization of the other hyperparameters. …
- 39.6k
2
votes
Using the standard deviation in Cross Validation
One variable at a time is not going to work unless you know the hyperparameter in question does not interact with other of your hyperparameters. …
- 39.6k
2
votes
"Continuity" of SVM as a function of hyperparameters
A model that does not change much if a small part of the training data is exchanged against a few other training cases is called stable, and I'd extend this also to hyperparameters.
You can check th …
- 39.6k
2
votes
Accepted
Can different classification methods be compared in the same manner as models during hyper-p...
Think of optimizing the "training algorithm" hyperparameter. …
- 39.6k
2
votes
Accepted
Hyperparameters tuning on resampled validation set?
latter is IMHO crucial if the result is to be used for optimization: while for a final model stability may be established in several ways and I'm OK with saying that only stable models will be considered, hyperparameter …
- 39.6k
2
votes
Seed in a grid search
It seems straightforward, that you ONLY want to test the parameters, and the less variance, the better
Well, it isn't that straightforward. @MatthewGunn already explained that it typically won't …
- 39.6k
2
votes
Accepted
Bagging models with different metaparameters versus cross validation?
From a certain point of view, optimizing the meta- or hyperparameter is the opposite approach to ensemble models. They are good for sifferent situations. … In that case, you may want to combine both approaches and build a bagged predictor with the optimized hyperparameter set. …
- 39.6k