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- "description": "Probability calibration with isotonic regression or logistic regression.\n\nThis class uses cross-validation to both estimate the parameters of a\nclassifier and subsequently calibrate a classifier. With default\n`ensemble=True`, for each cv split it\nfits a copy of the base estimator to the training subset, and calibrates it\nusing the testing subset. For prediction, predicted probabilities are\naveraged across these individual calibrated classifiers. When\n`ensemble=False`, cross-validation is used to obtain unbiased predictions,\nvia :func:`~sklearn.model_selection.cross_val_predict`, which are then\nused for calibration. For prediction, the base estimator, trained using all\nthe data, is used. This is the prediction method implemented when\n`probabilities=True` for :class:`~sklearn.svm.SVC` and :class:`~sklearn.svm.NuSVC`\nestimators (see :ref:`User Guide <scores_probabilities>` for details).\n\nAlready fitted classifiers can be calibrated via the parameter\n`cv=\"prefit\"`. In this case, no cross-validation is used and all provided\ndata is used for calibration. The user has to take care manually that data\nfor model fitting and calibration are disjoint.\n\nThe calibration is based on the :term:`decision_function` method of the\n`estimator` if it exists, else on :term:`predict_proba`.\n\nRead more in the :ref:`User Guide <calibration>`.\n",
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+ "description": "Probability calibration with isotonic regression or logistic regression.\n\nThis class uses cross-validation to both estimate the parameters of a\nclassifier and subsequently calibrate a classifier. With default\n`ensemble=True`, for each cv split it\nfits a copy of the base estimator to the training subset, and calibrates it\nusing the testing subset. For prediction, predicted probabilities are\naveraged across these individual calibrated classifiers. When\n`ensemble=False`, cross-validation is used to obtain unbiased predictions,\nvia :func:`~sklearn.model_selection.cross_val_predict`, which are then\nused for calibration. For prediction, the base estimator, trained using all\nthe data, is used. This is the prediction method implemented when\n`probabilities=True` for :class:`~sklearn.svm.SVC` and :class:`~sklearn.svm.NuSVC`\nestimators (see :ref:`User Guide <scores_probabilities>` for details).\n\nAlready fitted classifiers can be calibrated via the parameter\n`cv=\"prefit\"`. In this case, no cross-validation is used and all provided\ndata is used for calibration. The user has to take care manually that data\nfor model fitting and calibration are disjoint.\n\nThe calibration is based on the :term:`decision_function` method of the\n`estimator` if it exists, else on :term:`predict_proba`.\n\nRead more in the :ref:`User Guide <calibration>`.\nIn order to learn more on the CalibratedClassifierCV class, see the\nfollowing calibration examples:\n:ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`,\n:ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`, and\n:ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`.\n",
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- "description": "Weight function used in prediction. Possible values:\n\n- 'uniform' : uniform weights. All points in each neighborhood\nare weighted equally.\n- 'distance' : weight points by the inverse of their distance.\nin this case, closer neighbors of a query point will have a\ngreater influence than neighbors which are further away.\n- [callable] : a user-defined function which accepts an\narray of distances, and returns an array of the same shape\ncontaining the weights.\n\nUniform weights are used by default.\n",
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+ "description": "Weight function used in prediction. Possible values:\n\n- 'uniform' : uniform weights. All points in each neighborhood\nare weighted equally.\n- 'distance' : weight points by the inverse of their distance.\nin this case, closer neighbors of a query point will have a\ngreater influence than neighbors which are further away.\n- [callable] : a user-defined function which accepts an\narray of distances, and returns an array of the same shape\ncontaining the weights.\n\nUniform weights are used by default.\n\nSee the following example for a demonstration of the impact of\ndifferent weighting schemes on predictions:\n:ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`.\n",
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Lutz Roeder