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.