PerOutputRegressor¶

A multi-output model that trains one independent regressor per output.

This model does not use the prediction of one output as a feature for the next. Each output is modelled by its own copy of the base regressor, trained independently. (This is the streaming equivalent of scikit-learn's MultiOutputRegressor).

The set of outputs isn't known from the start in a streaming setting, new regressors are instantiated on the fly, one per output key encountered in y.

Parameters¶

• model

  Type → base.Regressor

  The regression model used to make predictions for each target.

Examples¶

from river import evaluate
from river import linear_model
from river import metrics
from river import multioutput
from river import preprocessing
from river import stream

from sklearn import datasets

dataset = stream.iter_sklearn_dataset(
    dataset=datasets.load_linnerud(),
    shuffle=True,
    seed=42
)

model = multioutput.PerOutputRegressor(
    model=(
        preprocessing.StandardScaler() |
        linear_model.LinearRegression(intercept_lr=0.3)
    )
)

metric = metrics.multioutput.MicroAverage(metrics.MAE())

evaluate.progressive_val_score(dataset, model, metric)

MicroAverage(MAE): 12.68377

Methods¶