PAClassifier¶
Passive-aggressive learning for classification.
Parameters¶
-
C
Default →
1.0 -
mode
Default →
1 -
learn_intercept
Default →
True
Examples¶
The following example is taken from this blog post.
from river import linear_model
from river import metrics
from river import stream
import numpy as np
from sklearn import datasets
from sklearn import model_selection
np.random.seed(1000)
X, y = datasets.make_classification(
n_samples=5000,
n_features=4,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_classes=2,
n_clusters_per_class=2
)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X,
y,
test_size=0.35,
random_state=1000
)
model = linear_model.PAClassifier(
C=0.01,
mode=1
)
for xi, yi in stream.iter_array(X_train, y_train):
y_pred = model.learn_one(xi, yi)
metric = metrics.Accuracy() + metrics.LogLoss()
for xi, yi in stream.iter_array(X_test, y_test):
metric = metric.update(yi, model.predict_proba_one(xi))
print(metric)
Accuracy: 88.46%, LogLoss: 0.325727
Methods¶
learn_one
Update the model with a set of features x and a label y.
Parameters
- x
- y
Returns
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
Returns
A dictionary that associates a probability which each label.