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Gradient Boosting for regression. This node has been automatically generated by wrapping the ``sklearn.ensemble.gradient_boosting.GradientBoostingRegressor`` 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 a regression tree is fit on the negative gradient of the given loss function. Read more in the :ref:`User Guide <gradient_boosting>`. **Parameters** loss : {'ls', 'lad', 'huber', 'quantile'}, optional (default='ls') loss function to be optimized. 'ls' refers to least squares regression. 'lad' (least absolute deviation) is a highly robust loss function solely based on order information of the input variables. 'huber' is a combination of the two. 'quantile' allows quantile regression (use `alpha` to specify the quantile). 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=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. alpha : float (default=0.9) The alphaquantile of the huber loss function and the quantile loss function. Only if ``loss='huber'`` or ``loss='quantile'``. 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 optional parameter *presort*. **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, 1] The collection of fitted subestimators. See also DecisionTreeRegressor, RandomForestRegressor **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: 

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Gradient Boosting for regression. This node has been automatically generated by wrapping the ``sklearn.ensemble.gradient_boosting.GradientBoostingRegressor`` 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 a regression tree is fit on the negative gradient of the given loss function. Read more in the :ref:`User Guide <gradient_boosting>`. **Parameters** loss : {'ls', 'lad', 'huber', 'quantile'}, optional (default='ls') loss function to be optimized. 'ls' refers to least squares regression. 'lad' (least absolute deviation) is a highly robust loss function solely based on order information of the input variables. 'huber' is a combination of the two. 'quantile' allows quantile regression (use `alpha` to specify the quantile). 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=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. alpha : float (default=0.9) The alphaquantile of the huber loss function and the quantile loss function. Only if ``loss='huber'`` or ``loss='quantile'``. 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 optional parameter *presort*. **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, 1] The collection of fitted subestimators. See also DecisionTreeRegressor, RandomForestRegressor **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.




DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead. This node has been automatically generated by wrapping the ``sklearn.ensemble.gradient_boosting.GradientBoostingRegressor`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Reduce X to its most important features. Uses ``coef_`` or ``feature_importances_`` to determine the most important features. For models with a ``coef_`` for each class, the absolute sum over the classes is used. Parameters  X : array or scipy sparse matrix of shape [n_samples, n_features] The input samples. threshold : string, float or None, optional (default=None) The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If "median" (resp. "mean"), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., "1.25*mean") may also be used. If None and if available, the object attribute ``threshold`` is used. Otherwise, "mean" is used by default. Returns  X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features.



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

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