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ALMAClassifier

Approximate Large Margin Algorithm (ALMA).

Parameters

  • p

    Type → float

    Default → 2

  • alpha

    Type → float

    Default → 0.9

  • B

    Type → float

    Default → 1.1111111111111112

  • C

    Type → float

    Default → 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: 82.56%

Methods

learn_one

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

Parameters

  • x — dict[base.typing.FeatureName, typing.Any]
  • y — base.typing.ClfTarget

predict_one

Predict the label of a set of features x.

Parameters

  • x — dict[base.typing.FeatureName, Any]
  • kwargs — Any

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[base.typing.FeatureName, float]
  • kwargs — typing.Any

Returns

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

References