User guide

The module

The module contains a scikit-learn compatible of fuzzy decision tree estimator. Currently, only single-output multiclass classification is supported.

Classifier

Similarly to the classification tree known from scikit-learn, the FuzzyDecisionTreeClassifier implements fit, predict, predict_proba and predict_log_proba methods:

  • at fit, some parameters can be learned from X and y. One can also provide weights for each sample in X. It will be treated as a fuzzy membership function on universe of samples;

  • at predict, predictions will be computed using X and the parameters learned during fit. The output corresponds to the predicted class for each sample;

  • predict_proba will give a 2D matrix where each column corresponds to the class and each entry will be the probability of the associated class.

  • predict_log_proba will give a logarithm of predict_proba.

FuzzyDecisionTreeClassifier inherits from both sklearn.base.BaseEstimator and sklearn.base.ClassifierMixin, what enables it to be used with scikit-learn objects, for example a pipeline:

>>> from sklearn.datasets import load_iris
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from fuzzytree import FuzzyDecisionTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> pipe = make_pipeline(StandardScaler(), FuzzyDecisionTreeClassifier())
>>> pipe.fit(X, y)
Pipeline(...)

Then, you can call predict and predict_proba:

>>> pipe.predict(X)
array([...])
>>> pipe.predict_proba(X)
array([...])

Since our classifier inherits from sklearn.base.ClassifierMixin, we can compute the accuracy by calling the score method:

>>> pipe.score(X, y)
0...

Regressor

As for now, regression is not supported :(