| Issue | #Downvotes for this reason | By |
|---|
| base_estimator | sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)(2) | The classifier whose output need to be calibrated to provide more accurate `predict_proba` outputs |
| base_estimator | The classifier whose output need to be calibrated to provide more accurate `predict_proba` outputs | default: {"oml-python:serialized_object": "component_reference", "value": {"key": "base_estimator", "step_name": null}} |
| cv | Determines the cross-validation splitting strategy
Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices
For integer/None inputs, if ``y`` is binary or multiclass,
:class:`sklearn.model_selection.StratifiedKFold` is used. If ``y`` is
neither binary nor multiclass, :class:`sklearn.model_selection.KFold`
is used
Refer :ref:`User Guide | default: 5 |
| method | The method to use for calibration. Can be 'sigmoid' which corresponds to Platt's method (i.e. a logistic regression model) or 'isotonic' which is a non-parametric approach. It is not advised to use isotonic calibration with too few calibration samples ``(<<1000)`` since it tends to overfit | default: "sigmoid" |