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A decision tree classifier. This node has been automatically generated by wrapping the ``sklearn.tree.tree.DecisionTreeClassifier`` 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="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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=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`. 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. class_weight : dict, list of dicts, "balanced" or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multioutput problems, a list of dicts can be provided in the same order as the columns of y. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multioutput, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. 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** ``classes_`` : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multioutput problem). ``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_classes_`` : int or list The number of classes (for single output problems), or a list containing the number of classes for each output (for multioutput problems). ``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 DecisionTreeRegressor **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_iris >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris.data, iris.target, cv=10) ... # doctest: +SKIP ... array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., 0.93..., 0.93..., 1. , 0.93..., 1. ])














Inherited from Inherited from Inherited from 

Inherited from ClassifierCumulator  







Inherited from ClassifierNode  










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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

A decision tree classifier. This node has been automatically generated by wrapping the ``sklearn.tree.tree.DecisionTreeClassifier`` 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="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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=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`. 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. class_weight : dict, list of dicts, "balanced" or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multioutput problems, a list of dicts can be provided in the same order as the columns of y. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multioutput, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. 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** ``classes_`` : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multioutput problem). ``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_classes_`` : int or list The number of classes (for single output problems), or a list containing the number of classes for each output (for multioutput problems). ``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 DecisionTreeRegressor **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_iris >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris.data, iris.target, cv=10) ... # doctest: +SKIP ... array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., 0.93..., 0.93..., 1. , 0.93..., 1. ])



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



Predict class or regression value for X. This node has been automatically generated by wrapping the sklearn.tree.tree.DecisionTreeClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned. Parameters
Returns

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

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