PARegressor¶
Passive-aggressive learning for regression.
Parameters¶
-
C – defaults to
1.0 -
mode – defaults to
1 -
eps – defaults to
0.1 -
learn_intercept – defaults to
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
>>> 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 = model.learn_one(xi, yi)
... metric = 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
Returns
self
predict_one
Predict the output of features x.
Parameters
- x
Returns
The prediction.