Comments (2)
I'm ok with this as it is necessary for grid search etc but I think it should not be the default way of using FooCV objects. For a new users it is error prone as if you fit with 2 different X and y (eg different sizes) you get an error. In basic examples we should always write it fit(X, y, cv=cv).
from scikit-learn.
seem to be fixed.
from scikit-learn.
Related Issues (20)
- Implement temperature scaling for (multi-class) calibration HOT 12
- GridSearchCV do not weight the score by the size of the fold when providing custom split for CV HOT 5
- RFECV docstring does not state how the `cv_results_` attribute is ordered by HOT 1
- Macro vs micro-averaging switched up in user guide HOT 9
- `DecisionTreeClassifier` does not handle `Nan` HOT 1
- ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️ HOT 5
- Missing _ZdlPv symbol in _argkmin_classmode for manylinux wheels produced by meson HOT 3
- MAINT(?) In `_tree.pyx` and `_splitter.pyx` for `tree/`, is `n_constant_features` necessary to pass around explicitly? HOT 7
- TypeError: cpu_count() got an unexpected keyword argument 'only_physical_cores' HOT 3
- Print warning if user passed only one class into StratifiedKFold HOT 2
- DOC: update FAQs to add permission using images HOT 1
- Error compiling with GCC14 in i686 HOT 3
- Add a download_openml util HOT 2
- Add an option handle_unknown="warn" in OneHotEncoder HOT 2
- BUG: ArgKmin64 on Windows with scipy 1.13rc1 or 1.14.dev times out HOT 24
- Make RFE/RFECV preserve pandas dataframes HOT 7
- HDBSCAN error with metric cosine HOT 5
- [Bug] `sklearn.base.BaseEstimator` subclasses not decoratable HOT 12
- Nightly CI failure when running Cython on the `nogil` build HOT 1
- KMeans clustering with [0, 1]-entries feature vectors outputs centroids with entries outside [0, 1] HOT 1
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