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
predict_one
Predict the output of features x
.
Parameters
- x
- kwargs
Returns
The prediction.