# DecisionTreeClassifier¶

class ibex.sklearn.tree.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)

Bases: sklearn.tree.tree.DecisionTreeClassifier, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute feature_importances_.

Example:

>>> import pandas as pd
>>> import numpy as np
>>> from ibex.sklearn import datasets
>>> from ibex.sklearn.ensemble import RandomForestClassifier as PdRandomForestClassifier

>>> iris = datasets.load_iris()
>>> features = iris['feature_names']
>>> iris = pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=features+['class'])

>>> iris[features]
sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2
...

>>> clf =  PdRandomForestClassifier(random_state=42).fit(iris[features], iris['class'])
>>>
>>> clf.feature_importances_
sepal length (cm)    0.129268
sepal width (cm)     0.015822
petal length (cm)    0.444740
petal width (cm)     0.410169
dtype: float64


A decision tree classifier.

Read more in the User Guide.

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_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.
min_samples_split : int, float, optional (default=2)

The minimum number of samples required to split an internal node:

• If int, then consider min_samples_split as the minimum number.
• If float, then min_samples_split is a percentage and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

Changed in version 0.18: Added float values for percentages.

min_samples_leaf : int, float, optional (default=1)

The minimum number of samples required to be at a leaf node:

• If int, then consider min_samples_leaf as the minimum number.
• If float, then min_samples_leaf is a percentage and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

Changed in version 0.18: Added float values for percentages.

min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
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.

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.
max_leaf_nodes : int or None, optional (default=None)
Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.
min_impurity_decrease : float, optional (default=0.)

A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

The weighted impurity decrease equation is the following:

N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)


where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

New in version 0.19.

min_impurity_split : float,

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

Deprecated since version 0.19: min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19 and will be removed in 0.21. Use min_impurity_decrease instead.

class_weight : dict, list of dicts, “balanced” or None, default=None

Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

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 multi-output, 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.

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.
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 (multi-output 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 multi-output 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.

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and max_features=n_features, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state has to be fixed.

DecisionTreeRegressor

 [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
>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier(random_state=0)
>>> 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.      ])

apply(X, check_input=True)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns the index of the leaf that each sample is predicted as.

New in version 0.17.

X : array_like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X_leaves : array_like, shape = [n_samples,]
For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.
fit(X, y, sample_weight=None, check_input=True, X_idx_sorted=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

X : array-like or sparse matrix, shape = [n_samples, n_features]
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels) as integers or strings.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.
check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X_idx_sorted : array-like, shape = [n_samples, n_features], optional
The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don’t use this parameter unless you know what to do.
self : object
Returns self.
predict(X, check_input=True)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict class or regression value 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.

X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes, or the predict values.
predict_log_proba(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict class log-probabilities of the input samples X.

X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1. The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
predict_proba(X, check_input=True)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict class probabilities of the input samples X.

The predicted class probability is the fraction of samples of the same class in a leaf.

check_input : boolean, (default=True)
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_input : bool
Run check_array on X.
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.