Home  Trees  Indices  Help 



A decision tree regressor. This node has been automatically generated by wrapping the ``sklearn.tree.tree.DecisionTreeRegressor`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Read more in the :ref:`User Guide <tree>`. **Parameters** criterion : string, optional (default="mse") The function to measure the quality of a split. The only supported criterion is "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion. splitter : string, optional (default="best") The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. 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`. 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_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. Ignored if ``max_leaf_nodes`` is not None. min_samples_split : int, optional (default=2) The minimum number of samples required to split an internal node. min_samples_leaf : int, 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. max_leaf_nodes : int or None, optional (default=None) Grow a tree 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. 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, optional (default=False) Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training. **Attributes** ``feature_importances_`` : array of shape = [n_features] The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. ``max_features_`` : int, The inferred value of max_features. ``n_features_`` : int The number of features when ``fit`` is performed. ``n_outputs_`` : int The number of outputs when ``fit`` is performed. ``tree_`` : Tree object The underlying Tree object. See also DecisionTreeClassifier **References** .. [1] http://en.wikipedia.org/wiki/Decision_tree_learning .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. .. [4] L. Breiman, and A. Cutler, "Random Forests", http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm **Examples** >>> from sklearn.datasets import load_boston >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeRegressor >>> boston = load_boston() >>> regressor = DecisionTreeRegressor(random_state=0) >>> cross_val_score(regressor, boston.data, boston.target, cv=10) ... # doctest: +SKIP ... array([ 0.61..., 0.57..., 0.34..., 0.41..., 0.75..., 0.07..., 0.29..., 0.33..., 1.42..., 1.77...])














Inherited from Inherited from 

Inherited from Cumulator  





Inherited from Node  


































































Inherited from 

Inherited from Node  

_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

A decision tree regressor. This node has been automatically generated by wrapping the ``sklearn.tree.tree.DecisionTreeRegressor`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Read more in the :ref:`User Guide <tree>`. **Parameters** criterion : string, optional (default="mse") The function to measure the quality of a split. The only supported criterion is "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion. splitter : string, optional (default="best") The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. 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`. 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_depth : int or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. Ignored if ``max_leaf_nodes`` is not None. min_samples_split : int, optional (default=2) The minimum number of samples required to split an internal node. min_samples_leaf : int, 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. max_leaf_nodes : int or None, optional (default=None) Grow a tree 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. 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, optional (default=False) Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training. **Attributes** ``feature_importances_`` : array of shape = [n_features] The feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. ``max_features_`` : int, The inferred value of max_features. ``n_features_`` : int The number of features when ``fit`` is performed. ``n_outputs_`` : int The number of outputs when ``fit`` is performed. ``tree_`` : Tree object The underlying Tree object. See also DecisionTreeClassifier **References** .. [1] http://en.wikipedia.org/wiki/Decision_tree_learning .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. .. [4] L. Breiman, and A. Cutler, "Random Forests", http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm **Examples** >>> from sklearn.datasets import load_boston >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeRegressor >>> boston = load_boston() >>> regressor = DecisionTreeRegressor(random_state=0) >>> cross_val_score(regressor, boston.data, boston.target, cv=10) ... # doctest: +SKIP ... array([ 0.61..., 0.57..., 0.34..., 0.41..., 0.75..., 0.07..., 0.29..., 0.33..., 1.42..., 1.77...])




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.tree.tree.DecisionTreeRegressor`` 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.



Build a decision tree from the training set (X, y). This node has been automatically generated by wrapping the sklearn.tree.tree.DecisionTreeRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

Home  Trees  Indices  Help 


Generated by Epydoc 3.0.1 on Tue Mar 8 12:39:48 2016  http://epydoc.sourceforge.net 