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PARegressor

Passive-aggressive learning for regression.

Parameters

  • C

    Default1.0

  • mode

    Default1

  • eps

    Default0.1

  • learn_intercept

    DefaultTrue

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

np.random.seed(1000)
X, y = datasets.make_regression(n_samples=500, n_features=4)

model = linear_model.PARegressor(
    C=0.01,
    mode=2,
    eps=0.1,
    learn_intercept=False
)
metric = metrics.MAE() + metrics.MSE()

for xi, yi in stream.iter_array(X, y):
    y_pred = model.predict_one(xi)
    model.learn_one(xi, yi)
    metric.update(yi, y_pred)

print(metric)
MAE: 9.809402
MSE: 472.393532

Methods

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

  • x
  • y

predict_one

Predict the output of features x.

Parameters

  • x

Returns

The prediction.