I also get "artifact not found". And I agree with Daniel. Once you decompose what the code is doing you realize that it does the job. The simplicity of the code to perform nested cross validation using scikit learn objects is impressive but I guess it also makes it less obvious. So making the example clearer by explaining what the code does or by adding a few comments can be useful for others.
Albert On Tue, 29 Nov 2016 at 11:19, Daniel Homola <daniel.homol...@imperial.ac.uk> wrote: > Hi Joel, > > Thanks a lot for the answer. > > "Each train/test split in cross_val_score holds out test data. > GridSearchCV then splits each train set into (inner-)train and validation > sets. " > > I know this is what nested CV supposed to do but the code is doing an > excellent job at obscuring this. I'll try and add some clarification in as > comments later today. > > Cheers, > > d > > > On 29/11/16 00:07, Joel Nothman wrote: > > If that clarifies, please offer changes to the example (as a pull request) > that make this clearer. > > On 29 November 2016 at 11:06, Joel Nothman <joel.noth...@gmail.com> wrote: > > Briefly: > > clf = GridSearchCV > <http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV>(estimator=svr, > param_grid=p_grid, cv=inner_cv)nested_score = cross_val_score > <http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score>(clf, > X=X_iris, y=y_iris, cv=outer_cv) > > > Each train/test split in cross_val_score holds out test data. GridSearchCV > then splits each train set into (inner-)train and validation sets. There is > no leakage of test set knowledge from the outer loop into the grid search > optimisation; no leakage of validation set knowledge into the SVR > optimisation. The outer test data are reused as training data, but within > each split are only used to measure generalisation error. > > Is that clear? > > On 29 November 2016 at 10:30, Daniel Homola <dani.hom...@gmail.com> wrote: > > Dear all, > > > I was wondering if the following example code is valid: > > > http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html > > My understanding is, that the point of nested cross-validation is to > prevent any data leakage from the inner grid-search/param optimization CV > loop into the outer model evaluation CV loop. This could be achieved if the > outer CV loop's test data is completely separated from the inner loop's CV, > as shown here: > > > https://mlr-org.github.io/mlr-tutorial/release/html/img/nested_resampling.png > > > The code in the above example however doesn't seem to achieve this in any > way. > > > Am I missing something here? > > > Thanks a lot, > > dh > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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