I would say there are 2 different applications of nested CV. You could use it
for algorithm selection (with hyperparam tuning in the inner loop). Or, you
could use it as an estimate of the generalization performance (only hyperparam
tuning), which has been reported to be less biased than the a k-fold CV
estimate (Varma, S., & Simon, R. (2006). Bias in error estimation when using
cross-validation for model selection. BMC Bioinformatics, 7, 91.
http://doi.org/10.1186/1471-2105-7-91)
By "you could use it as an estimate of the generalization performance (only
hyperparam tuning)” I mean as a replacement for k-fold on the training set and
evaluation on an independent test set.
> On May 12, 2016, at 4:16 PM, Алексей Драль <aad...@gmail.com> wrote:
>
> Hi Amita,
>
> As far as I understand your question, you only need one CV loop to optimize
> your objective with scoring function provided:
>
> ===
> pipeline=Pipeline([('scale', preprocessing.StandardScaler()),('filter',
> SelectKBest(f_regression)),('svr', svm.SVR())]
> C_range = [0.1, 1, 10, 100]
> gamma_range=numpy.logspace(-2, 2, 5)
> param_grid=[{'svr__kernel': ['rbf'], 'svr__gamma': gamma_range,'svr__C':
> C_range}]
> grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5,
> scoring=scoring_function)
> grid_search.fit(X_train, Y_train)
> ===
>
> More details about it you should be able to find in documentation:
> • http://scikit-learn.org/stable/modules/grid_search.html#grid-search
> •
> http://scikit-learn.org/stable/modules/grid_search.html#gridsearch-scoring
>
> 2016-05-12 17:05 GMT+01:00 Amita Misra <amis...@ucsc.edu>:
> Hi,
>
> I have a limited dataset and hence want to learn the parameters and also
> evaluate the final model.
> From the documents it looks that nested cross validation is the way to do it.
> I have the code but still I want to be sure that I am not overfitting any way.
>
> pipeline=Pipeline([('scale', preprocessing.StandardScaler()),('filter',
> SelectKBest(f_regression)),('svr', svm.SVR())]
> C_range = [0.1, 1, 10, 100]
> gamma_range=numpy.logspace(-2, 2, 5)
> param_grid=[{'svr__kernel': ['rbf'], 'svr__gamma': gamma_range,'svr__C':
> C_range}]
> grid_search = GridSearchCV(pipeline, param_grid=param_grid,cv=5)
> Y_pred=cross_validation.cross_val_predict(grid_search, X_train, Y_train,cv=10)
>
> correlation= numpy.ma.corrcoef(Y_train,Y_pred)[0, 1]
>
>
> please let me know if my understanding is correct.
>
> This is 10*5 nested cross validation. Inner folds CV over training data
> involves a grid search over hyperparameters and outer folds evaluate the
> performance.
>
>
>
> Thanks,
> Amita--
> Amita Misra
> Graduate Student Researcher
> Natural Language and Dialogue Systems Lab
> Baskin School of Engineering
> University of California Santa Cruz
>
>
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> --
> Yours sincerely,
> Alexey A. Dral
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