Class CalibratedClassifierCVScikitsLearnNode

Probability calibration with isotonic regression or sigmoid.

This node has been automatically generated by wrapping the ``sklearn.calibration.CalibratedClassifierCV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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 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-parameteric 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 3-fold cross-validation,
    - integer, to specify the number of folds.
    - An object to be used as a cross-validation generator.
    - An iterable yielding train/test splits.

    For integer/None inputs, if ``y`` is binary or multiclass,
    :class:`StratifiedKFold` used. If ``y`` is neither binary nor
    multiclass, :class:`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.

**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

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict the target of new samples. Can be different from the prediction of the uncalibrated classifier.

This node has been automatically generated by wrapping the sklearn.calibration.CalibratedClassifierCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape (n_samples, n_features)

The samples.

Returns

C : array, shape (n_samples,)

The predicted class.

Overrides: ClassifierNode.label

Fit the calibrated model

This node has been automatically generated by wrapping the sklearn.calibration.CalibratedClassifierCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape (n_samples, n_features)

Training data.

y : array-like, shape (n_samples,)

Target values.

sample_weight : array-like, shape = [n_samples] or None

Sample weights. If None, then samples are equally weighted.

Returns

self : object

Returns an instance of self.

Overrides: Node.stop_training