8.27.1. sklearn.tree.DecisionTreeClassifier¶

class sklearn.tree.DecisionTreeClassifier(criterion='gini', max_depth=None, min_samples_split=1, min_samples_leaf=1, min_density=0.1, max_features=None, compute_importances=False, random_state=None)¶

A decision tree classifier.

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.

max_depth : integer 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.

min_samples_split : integer, optional (default=1)

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_density : float, optional (default=0.1)

This parameter controls a trade-off in an optimization heuristic. It controls the minimum density of the sample_mask (i.e. the fraction of samples in the mask). If the density falls below this threshold the mask is recomputed and the input data is packed which results in data copying. If min_density equals to one, the partitions are always represented as copies of the original data. Otherwise, partitions are represented as bit masks (aka sample masks).

max_features : int, string or None, optional (default=None)

The number of features to consider when looking for the best split. If “auto”, then max_features=sqrt(n_features) on classification tasks and max_features=n_features on regression problems. If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features.

compute_importances : boolean, optional (default=True)

Whether feature importances are computed and stored into the feature_importances_ attribute when calling fit.

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.

See also

DecisionTreeRegressor

References

[R77]

http://en.wikipedia.org/wiki/Decision_tree_learning

[R78]

L. Breiman, J. Friedman, R. Olshen, and C. Stone, “Classification and Regression Trees”, Wadsworth, Belmont, CA, 1984.

[R79]

T. Hastie, R. Tibshirani and J. Friedman. “Elements of Statistical Learning”, Springer, 2009.

[R80]

(1, 2) 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)
...                             
...
array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,
        0.93...,  0.93...,  1.     ,  0.93...,  1.      ])

Attributes

tree_	Tree object	The underlying Tree object.
feature_importances_	array of shape = [n_features]	The feature mportances (the higher, the more important the feature). The importance I(f) of a feature f is computed as the (normalized) total reduction of error brought by that feature. It is also known as the Gini importance [R80]. $I(f) = \sum_{nodes A for which f is used} n_samples(A) * \Delta err$

Methods

`fit`(X, y[, sample_mask, X_argsorted])	Build a decision tree from the training set (X, y).
`fit_transform`(X[, y])	Fit to data, then transform it
`get_params`([deep])	Get parameters for the estimator
`predict`(X)	Predict class or regression target for X.
`predict_log_proba`(X)	Predict class log-probabilities of the input samples X.
`predict_proba`(X)	Predict class probabilities of the input samples X.
`score`(X, y)	Returns the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of the estimator.
`transform`(X[, threshold])	Reduce X to its most important features.

__init__(criterion='gini', max_depth=None, min_samples_split=1, min_samples_leaf=1, min_density=0.1, max_features=None, compute_importances=False, random_state=None)¶

fit(X, y, sample_mask=None, X_argsorted=None)¶

Build a decision tree from the training set (X, y).

Parameters :

X : array-like of shape = [n_samples, n_features]

The training input samples.

y : array-like, shape = [n_samples]

The target values (integers that correspond to classes in classification, real numbers in regression).

Returns :

self : object

Returns self.

fit_transform(X, y=None, **fit_params)¶

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

get_params(deep=True)¶

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)¶

Predict class or regression target for X.

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 :

X : array-like of shape = [n_samples, n_features]

The input samples.

Returns :

y : array of shape = [n_samples]

The predicted classes, or the predict values.

predict_log_proba(X)¶

Predict class log-probabilities of the input samples X.

Parameters :

X : array-like of shape = [n_samples, n_features]

The input samples.

Returns :

p : array of shape = [n_samples, n_classes]

The class log-probabilities of the input samples. Classes are ordered by arithmetical order.

predict_proba(X)¶

Predict class probabilities of the input samples X.

Parameters :

X : array-like of shape = [n_samples, n_features]

The input samples.

Returns :

p : array of shape = [n_samples, n_classes]

The class probabilities of the input samples. Classes are ordered by arithmetical order.

score(X, y)¶

Returns the mean accuracy on the given test data and labels.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float

set_params(**params)¶

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :	self :

transform(X, threshold=None)¶

Reduce X to its most important features.

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.

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8.27.1. sklearn.tree.DecisionTreeClassifier¶