PredClipper

Clips the target after predicting.

Parameters

• regressor

  Type → base.Regressor

  Regressor model for which to clip the predictions.

• y_min

  Type → float

  minimum value.

• y_max

  Type → float

  maximum value.

Examples

from river import linear_model
from river import preprocessing

dataset = (
    ({'a': 2, 'b': 4}, 80),
    ({'a': 3, 'b': 5}, 100),
    ({'a': 4, 'b': 6}, 120)
)

model = preprocessing.PredClipper(
    regressor=linear_model.LinearRegression(),
    y_min=0,
    y_max=200
)

for x, y in dataset:
    _ = model.learn_one(x, y)

model.predict_one({'a': -100, 'b': -200})

0

model.predict_one({'a': 50, 'b': 60})

200

Methods

learn_one

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

Parameters

• x
• y
• kwargs

Returns

self

predict_one

Predict the output of features x.

Parameters

• x
• kwargs

Returns

The prediction.