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ALMAClassifier

Approximate Large Margin Algorithm (ALMA).

Parameters

  • p – defaults to 2

  • alpha – defaults to 0.9

  • B – defaults to 1.1111111111111112

  • C – defaults to 1.4142135623730951

Attributes

  • w (collections.defaultdict)

    The current weights.

  • k (int)

    The number of instances seen during training.

Examples

>>> from river import datasets
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing

>>> dataset = datasets.Phishing()

>>> model = (
...     preprocessing.StandardScaler() |
...     linear_model.ALMAClassifier()
... )

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 82.64%

Methods

learn_one

Update the model with a set of features x and a label y.

Parameters

  • x (dict)
  • y (Union[bool, str, int])

Returns

Classifier: self

predict_one

Predict the label of a set of features x.

Parameters

  • x (dict)
  • kwargs

Returns

typing.Union[bool, str, int, NoneType]: The predicted label.

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

  • x (dict)

Returns

typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.

References