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We've been talking about nested cross-validation. Scikit-learn actually has a nice example of this. In essence, it looks like this:
inner_cv=KFold(n_splits=4, shuffle=True, random_state=i)
outer_cv=KFold(n_splits=4, shuffle=True, random_state=i)
clf=GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)
# Note that the gridsearch instance is passed into the outer cvscore=cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv).mean()
It would be nice if we could eventually do the same, but for that we need to make sure our model/pipeline can work with xarray data structures.
We've been talking about nested cross-validation. Scikit-learn actually has a nice example of this. In essence, it looks like this:
It would be nice if we could eventually do the same, but for that we need to make sure our model/pipeline can work with xarray data structures.
related to #71 AI4S2S/lilio#46
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