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PredClipper

Clips the target after predicting.

Parameters

  • regressor (base.Regressor)

    Regressor model for which to clip the predictions.

  • y_min (float)

    minimum value.

  • y_max (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 (dict)
  • y (numbers.Number)
  • kwargs

Returns

Regressor: self

predict_one

Predict the output of features x.

Parameters

  • x (dict)
  • kwargs

Returns

Number: The prediction.