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.

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.

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_.

__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]
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_.

set_fit_request(*, check_input: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → FuzzyDecisionTreeClassifier

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

set_predict_log_proba_request(*, check_input: bool | None | str = '$UNCHANGED$') → FuzzyDecisionTreeClassifier

Request metadata passed to the predict_log_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict_log_proba if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict_log_proba.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in predict_log_proba.

Returns:
selfobject

The updated object.

set_predict_proba_request(*, check_input: bool | None | str = '$UNCHANGED$') → FuzzyDecisionTreeClassifier

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict_proba.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in predict_proba.

Returns:
selfobject

The updated object.

set_predict_request(*, check_input: bool | None | str = '$UNCHANGED$') → FuzzyDecisionTreeClassifier

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in predict.

Returns:
selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → FuzzyDecisionTreeClassifier

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

Examples using fuzzytree.FuzzyDecisionTreeClassifier

Plotting Fuzzy Decision Tree Classifier

Plotting Fuzzy Decision Tree Classifier

Comparison of crisp and fuzzy classifiers on make_moons dataset

Comparison of crisp and fuzzy classifiers on make_moons dataset

Comparison of crisp and fuzzy classifiers on make_circles dataset

Comparison of crisp and fuzzy classifiers on make_circles dataset

Comparison of crisp and fuzzy classifiers on iris dataset

Comparison of crisp and fuzzy classifiers on iris dataset

Comparison of crisp and fuzzy classifiers on make_blobs dataset

Comparison of crisp and fuzzy classifiers on make_blobs dataset