val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?base_estimator:Py.Object.t ->
?method_:[ `Sigmoid | `Isotonic ] ->
?cv:
[ `Int of int
| `CrossValGenerator of Py.Object.t
| `Ndarray of Ndarray.t
| `Prefit ] ->
unit ->
tProbability calibration with isotonic regression or sigmoid.
See glossary entry for :term:`cross-validation estimator`.
With this class, the base_estimator is fit on the train set of the cross-validation generator and the test set is used for calibration. The probabilities for each of the folds are then averaged for prediction. In case that cv="prefit" is passed to __init__, it is assumed that base_estimator has been fitted already and all data is used for calibration. Note that data for fitting the classifier and for calibrating it must be disjoint.
Read more in the :ref:`User Guide <calibration>`.
Parameters ---------- base_estimator : instance BaseEstimator The classifier whose output decision function needs to be calibrated to offer more accurate predict_proba outputs. If cv=prefit, the classifier must have been fit already on data.
method : 'sigmoid' or 'isotonic' The method to use for calibration. Can be 'sigmoid' which corresponds to Platt's method 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. Use sigmoids (Platt's calibration) in this case.
cv : integer, cross-validation generator, iterable or "prefit", optional Determines the cross-validation splitting strategy. Possible inputs for cv are:
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 <cross_validation>` for the various cross-validation strategies that can be used here.
If "prefit" is passed, it is assumed that base_estimator has been fitted already and all data is used for calibration.
.. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold.
Attributes ---------- classes_ : array, shape (n_classes) The class labels.
calibrated_classifiers_ : list (len() equal to cv or 1 if cv == "prefit") The list of calibrated classifiers, one for each crossvalidation fold, which has been fitted on all but the validation fold and calibrated on the validation fold.
References ---------- .. 1 Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001
.. 2 Transforming Classifier Scores into Accurate Multiclass Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)
.. 3 Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, J. Platt, (1999)
.. 4 Predicting Good Probabilities with Supervised Learning, A. Niculescu-Mizil & R. Caruana, ICML 2005
Fit the calibrated model
Parameters ---------- X : array-like, shape (n_samples, n_features) Training data.
y : array-like, shape (n_samples,) Target values.
sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted.
Returns ------- self : object Returns an instance of self.
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Predict the target of new samples. Can be different from the prediction of the uncalibrated classifier.
Parameters ---------- X : array-like, shape (n_samples, n_features) The samples.
Returns ------- C : array, shape (n_samples,) The predicted class.
Posterior probabilities of classification
This function returns posterior probabilities of classification according to each class on an array of test vectors X.
Parameters ---------- X : array-like, shape (n_samples, n_features) The samples.
Returns ------- C : array, shape (n_samples, n_classes) The predicted probas.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val calibrated_classifiers_ : t -> Py.Object.tAttribute calibrated_classifiers_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.