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A Bagging classifier. This node has been automatically generated by wrapping the ``sklearn.ensemble.bagging.BaggingClassifier`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. A Bagging classifier is an ensemble metaestimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a metaestimator can typically be used as a way to reduce the variance of a blackbox estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. Read more in the :ref:`User Guide <bagging>`. **Parameters** base_estimator : object or None, optional (default=None) The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree. n_estimators : int, optional (default=10) The number of base estimators in the ensemble. max_samples : int or float, optional (default=1.0) The number of samples to draw from X to train each base estimator.  If int, then draw `max_samples` samples.  If float, then draw `max_samples * X.shape[0]` samples. max_features : int or float, optional (default=1.0) The number of features to draw from X to train each base estimator.  If int, then draw `max_features` features.  If float, then draw `max_features * X.shape[1]` features. bootstrap : boolean, optional (default=True) Whether samples are drawn with replacement. bootstrap_features : boolean, optional (default=False) Whether features are drawn with replacement. oob_score : bool Whether to use outofbag samples to estimate the generalization error. warm_start : bool, optional (default=False) When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. .. versionadded:: 0.17 *warm_start* constructor parameter. n_jobs : int, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If 1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the building process. **Attributes** ``base_estimator_`` : list of estimators The base estimator from which the ensemble is grown. ``estimators_`` : list of estimators The collection of fitted base estimators. ``estimators_samples_`` : list of arrays The subset of drawn samples (i.e., the inbag samples) for each base estimator. ``estimators_features_`` : list of arrays The subset of drawn features for each base estimator. ``classes_`` : array of shape = [n_classes] The classes labels. ``n_classes_`` : int or list The number of classes. ``oob_score_`` : float Score of the training dataset obtained using an outofbag estimate. ``oob_decision_function_`` : array of shape = [n_samples, n_classes] Decision function computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. **References** .. [1] L. Breiman, "Pasting small votes for classification in large databases and online", Machine Learning, 36(1), 85103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346361, 2012.














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

A Bagging classifier. This node has been automatically generated by wrapping the ``sklearn.ensemble.bagging.BaggingClassifier`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. A Bagging classifier is an ensemble metaestimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a metaestimator can typically be used as a way to reduce the variance of a blackbox estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. Read more in the :ref:`User Guide <bagging>`. **Parameters** base_estimator : object or None, optional (default=None) The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree. n_estimators : int, optional (default=10) The number of base estimators in the ensemble. max_samples : int or float, optional (default=1.0) The number of samples to draw from X to train each base estimator.  If int, then draw `max_samples` samples.  If float, then draw `max_samples * X.shape[0]` samples. max_features : int or float, optional (default=1.0) The number of features to draw from X to train each base estimator.  If int, then draw `max_features` features.  If float, then draw `max_features * X.shape[1]` features. bootstrap : boolean, optional (default=True) Whether samples are drawn with replacement. bootstrap_features : boolean, optional (default=False) Whether features are drawn with replacement. oob_score : bool Whether to use outofbag samples to estimate the generalization error. warm_start : bool, optional (default=False) When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. .. versionadded:: 0.17 *warm_start* constructor parameter. n_jobs : int, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If 1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the building process. **Attributes** ``base_estimator_`` : list of estimators The base estimator from which the ensemble is grown. ``estimators_`` : list of estimators The collection of fitted base estimators. ``estimators_samples_`` : list of arrays The subset of drawn samples (i.e., the inbag samples) for each base estimator. ``estimators_features_`` : list of arrays The subset of drawn features for each base estimator. ``classes_`` : array of shape = [n_classes] The classes labels. ``n_classes_`` : int or list The number of classes. ``oob_score_`` : float Score of the training dataset obtained using an outofbag estimate. ``oob_decision_function_`` : array of shape = [n_samples, n_classes] Decision function computed with outofbag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. **References** .. [1] L. Breiman, "Pasting small votes for classification in large databases and online", Machine Learning, 36(1), 85103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346361, 2012.



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



Predict class for X. This node has been automatically generated by wrapping the sklearn.ensemble.bagging.BaggingClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting. Parameters
Returns

Build a Bagging ensemble of estimators from the training set (X, y). This node has been automatically generated by wrapping the sklearn.ensemble.bagging.BaggingClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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