package sklearn

  1. Overview
  2. Docs
Legend:
Library
Module
Module type
Parameter
Class
Class type
type t
val of_pyobject : Py.Object.t -> t
val to_pyobject : t -> Py.Object.t
val create : ?base_estimator:Py.Object.t -> ?method_:[ `Sigmoid | `Isotonic ] -> ?cv: [ `Int of int | `CrossValGenerator of Py.Object.t | `Ndarray of Ndarray.t | `Prefit ] -> unit -> t

Probability 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:

  • 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 <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

val fit : ?sample_weight:Ndarray.t -> x:Ndarray.t -> y:Ndarray.t -> t -> t

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.t

Get 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.

val predict : x:Ndarray.t -> t -> Ndarray.t

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.

val predict_proba : x:Ndarray.t -> t -> Ndarray.t

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.

val score : ?sample_weight:Ndarray.t -> x:Ndarray.t -> y:Ndarray.t -> t -> float

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 -> t

Set 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 classes_ : t -> Ndarray.t

Attribute classes_: see constructor for documentation

val calibrated_classifiers_ : t -> Py.Object.t

Attribute calibrated_classifiers_: see constructor for documentation

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

OCaml

Innovation. Community. Security.