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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 crossvalidation 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 nonparameteric 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, crossvalidation generator, iterable or "prefit", optional Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,  integer, to specify the number of folds.  An object to be used as a crossvalidation 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 crossvalidation 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. NiculescuMizil & R. Caruana, ICML 2005














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

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 crossvalidation 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 nonparameteric 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, crossvalidation generator, iterable or "prefit", optional Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,  integer, to specify the number of folds.  An object to be used as a crossvalidation 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 crossvalidation 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. NiculescuMizil & R. Caruana, ICML 2005



Transform the data and labels lists to array objects and reshape them.



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
Returns

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
Returns

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