skmultiflow.core.
ClassifierMixin
Mixin class for all classifiers in scikit-multiflow.
Methods
fit(self, X, y[, classes, sample_weight])
fit
Fit the model.
partial_fit(self, X, y[, classes, sample_weight])
partial_fit
Partially (incrementally) fit the model.
predict(self, X)
predict
Predict classes for the passed data.
predict_proba(self, X)
predict_proba
Estimates the probability of each sample in X belonging to each of the class-labels.
score(self, X, y[, sample_weight])
score
Returns the mean accuracy on the given test data and labels.
The features to train the model.
An array-like with the class labels of all samples in X.
Contains all possible/known class labels. Usage varies depending on the learning method.
Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.
Array with all possible/known class labels. Usage varies depending on the learning method.
The set of data samples to predict the class labels for.
The matrix of samples one wants to predict the class probabilities for.
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.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.