`fuzzytree`.FuzzyDecisionTreeClassifier¶

class fuzzytree.FuzzyDecisionTreeClassifier(fuzziness=0.8, criterion='gini', max_depth=None, min_membership_split=2.0, min_membership_leaf=1.0, min_impurity_decrease=0.0)[source]¶

A fuzzy decision tree classifier. Read more in the User Guide.

Parameters

fuzzinessfloat, default=0.8

The fuzziness parameter that controls softness of the tree between 0. (hard) and 1. (soft).

criterion{“gini”, “entropy”, “misclassification”}, default=”gini”

The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity, “entropy” for the information gain and “misclassification” for the misclassification ratio.

max_depthint, 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_membership_split sum of membership or recursion limit is met.

min_membership_splitfloat, default=2.0

The minimum sum of membership required to split an internal node.

min_membership_leaffloat, default=1.0

The minimum sum of membership required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_membership_leaf sum of membership in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

min_impurity_decreasefloat, default=0.0

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:

impurity - M_t_R / M_t * right_impurity
         - M_t_L / M_t * left_impurity

where M_t_L is the sum of membership in the left child and M_t_R is the sum of membership in the right child.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_membership_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 predict() method operates using the numpy.argmax() function on the outputs of predict_proba(). This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in classes_.

Attributes

classes_ndarray of shape (n_classes,): The classes labels.
n_classes_int or list of int: 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. Currently, always equals 1.
tree_FuzzyTree: The underlying Tree object. Please refer to help(fuzzytree._fuzzy_tree.FuzzyTree) for attributes of Tree object and for basic usage of these attributes.

__init__(fuzziness=0.8, criterion='gini', max_depth=None, min_membership_split=2.0, min_membership_leaf=1.0, min_impurity_decrease=0.0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(X, y, sample_weight=None, check_input=True)[source]¶

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

Parameters

Xarray-like of shape (n_samples, n_features): The training input samples. Internally, it will be converted to dtype=np.float64.
yarray-like of shape (n_samples,): The target values (class labels) as integers or strings. Internally, it will be converted to an array-like of integers.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights used as initial membership function for samples. If None, then samples are equally weighted. Splits that would create child nodes with net zero membership are ignored while searching for a split in each node.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

selfFuzzyDecisionTreeClassifier: Fitted estimator.

predict_log_proba(X, check_input=True)[source]¶

Predict class log-probabilities of the input samples X.

Parameters

Xarray-like of shape (n_samples, n_features): The input samples. Internally, it will be converted to dtype=np.float64.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

probandarray of shape (n_samples, n_classes): 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]¶

Predict class probabilities of the input samples X. The predicted class probability is the fraction of membership of samples of the same class in a leaf.

Parameters

Xarray-like of shape (n_samples, n_features): The input samples. Internally, it will be converted to dtype=np.float64.
check_inputbool, default=True: Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

probandarray of shape (n_samples, n_classes): The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.