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Gradient Boosting for classification. This node has been automatically generated by wrapping the ``sklearn.ensemble.gradient_boosting.GradientBoostingClassifier`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. GB builds an additive model in a forward stagewise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced. Read more in the :ref:`User Guide <gradient_boosting>`. **Parameters** loss : {'deviance', 'exponential'}, optional (default='deviance') loss function to be optimized. 'deviance' refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss 'exponential' gradient boosting recovers the AdaBoost algorithm. learning_rate : float, optional (default=0.1) learning rate shrinks the contribution of each tree by `learning_rate`. There is a tradeoff between learning_rate and n_estimators. n_estimators : int (default=100) The number of boosting stages to perform. Gradient boosting is fairly robust to overfitting so a large number usually results in better performance. max_depth : integer, optional (default=3) maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. Ignored if ``max_leaf_nodes`` is not None. min_samples_split : integer, optional (default=2) The minimum number of samples required to split an internal node. min_samples_leaf : integer, optional (default=1) The minimum number of samples required to be at a leaf node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. subsample : float, optional (default=1.0) The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split:   If int, then consider `max_features` features at each split.   If float, then `max_features` is a percentage and  `int(max_features * n_features)` features are considered at each  split.   If "auto", then `max_features=sqrt(n_features)`.   If "sqrt", then `max_features=sqrt(n_features)`.   If "log2", then `max_features=log2(n_features)`.   If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None then ``max_depth`` will be ignored. init : BaseEstimator, None, optional (default=None) An estimator object that is used to compute the initial predictions. ``init`` has to provide ``fit`` and ``predict``. If None it uses ``loss.init_estimator``. verbose : int, default: 0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. warm_start : bool, default: False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. 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`. presort : bool or 'auto', optional (default='auto') Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error. .. versionadded:: 0.17 *presort* parameter. **Attributes** ``feature_importances_`` : array, shape = [n_features] The feature importances (the higher, the more important the feature). ``oob_improvement_`` : array, shape = [n_estimators] The improvement in loss (= deviance) on the outofbag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. ``train_score_`` : array, shape = [n_estimators] The ith score ``train_score_[i]`` is the deviance (= loss) of the model at iteration ``i`` on the inbag sample. If ``subsample == 1`` this is the deviance on the training data. ``loss_`` : LossFunction The concrete ``LossFunction`` object. init : BaseEstimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``. ``estimators_`` : ndarray of DecisionTreeRegressor, shape = [n_estimators, ``loss_.K``] The collection of fitted subestimators. ``loss_.K`` is 1 for binary classification, otherwise n_classes. See also sklearn.tree.DecisionTreeClassifier, RandomForestClassifier AdaBoostClassifier **References** J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Gradient Boosting for classification. This node has been automatically generated by wrapping the ``sklearn.ensemble.gradient_boosting.GradientBoostingClassifier`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. GB builds an additive model in a forward stagewise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced. Read more in the :ref:`User Guide <gradient_boosting>`. **Parameters** loss : {'deviance', 'exponential'}, optional (default='deviance') loss function to be optimized. 'deviance' refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss 'exponential' gradient boosting recovers the AdaBoost algorithm. learning_rate : float, optional (default=0.1) learning rate shrinks the contribution of each tree by `learning_rate`. There is a tradeoff between learning_rate and n_estimators. n_estimators : int (default=100) The number of boosting stages to perform. Gradient boosting is fairly robust to overfitting so a large number usually results in better performance. max_depth : integer, optional (default=3) maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. Ignored if ``max_leaf_nodes`` is not None. min_samples_split : integer, optional (default=2) The minimum number of samples required to split an internal node. min_samples_leaf : integer, optional (default=1) The minimum number of samples required to be at a leaf node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. subsample : float, optional (default=1.0) The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split:   If int, then consider `max_features` features at each split.   If float, then `max_features` is a percentage and  `int(max_features * n_features)` features are considered at each  split.   If "auto", then `max_features=sqrt(n_features)`.   If "sqrt", then `max_features=sqrt(n_features)`.   If "log2", then `max_features=log2(n_features)`.   If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. If not None then ``max_depth`` will be ignored. init : BaseEstimator, None, optional (default=None) An estimator object that is used to compute the initial predictions. ``init`` has to provide ``fit`` and ``predict``. If None it uses ``loss.init_estimator``. verbose : int, default: 0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. warm_start : bool, default: False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. 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`. presort : bool or 'auto', optional (default='auto') Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error. .. versionadded:: 0.17 *presort* parameter. **Attributes** ``feature_importances_`` : array, shape = [n_features] The feature importances (the higher, the more important the feature). ``oob_improvement_`` : array, shape = [n_estimators] The improvement in loss (= deviance) on the outofbag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. ``train_score_`` : array, shape = [n_estimators] The ith score ``train_score_[i]`` is the deviance (= loss) of the model at iteration ``i`` on the inbag sample. If ``subsample == 1`` this is the deviance on the training data. ``loss_`` : LossFunction The concrete ``LossFunction`` object. init : BaseEstimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``. ``estimators_`` : ndarray of DecisionTreeRegressor, shape = [n_estimators, ``loss_.K``] The collection of fitted subestimators. ``loss_.K`` is 1 for binary classification, otherwise n_classes. See also sklearn.tree.DecisionTreeClassifier, RandomForestClassifier AdaBoostClassifier **References** J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.



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.gradient_boosting.GradientBoostingClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

Fit the gradient boosting model. This node has been automatically generated by wrapping the sklearn.ensemble.gradient_boosting.GradientBoostingClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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