Skip to content

ALMAClassifier

Approximate Large Margin Algorithm (ALMA).

Parameters

  • p

    Default2

  • alpha

    Default0.9

  • B

    Default1.1111111111111112

  • C

    Default1.4142135623730951

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'base.typing.ClfTarget'

Returns

Classifier: self

predict_one

Predict the label of a set of features x.

Parameters

  • x'dict'
  • kwargs

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_one

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

Parameters

  • x'dict'

Returns

dict[base.typing.ClfTarget, float]: A dictionary that associates a probability which each label.