ALMAClassifier¶
Approximate Large Margin Algorithm (ALMA).
Parameters¶
-
p
Default →
2 -
alpha
Default →
0.9 -
B
Default →
1.1111111111111112 -
C
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: 82.64%
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.