DummyRegressor

DummyRegressor makes predictions that ignore the input features.

Quickstart

from sktime.regression.dummy import DummyRegressor

estimator = DummyRegressor(strategy='mean', constant=None, quantile=None)

Parameters(3)

strategy{“mean”, “median”, “quantile”, “constant”}, default=”mean”

Strategy to use to generate predictions.

• “mean”: always predicts the mean of the training set

• “median”: always predicts the median of the training set

• “quantile”: always predicts a specified quantile of the training set,

provided with the quantile parameter. * “constant”: always predicts a constant value that is provided by the user.

constantint or float or array-like of shape (n_outputs,), default=None

The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy.

quantilefloat in [0.0, 1.0], default=None

The quantile to predict using the “quantile” strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum.

Examples

>>> from sktime.regression.dummy import DummyRegressor
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> regressor = DummyRegressor (strategy = "median")
>>> regressor. fit (X_train, y_train) DummyRegressor(strategy='median')
>>> y_pred = regressor. predict (X_test)