DummyRegressor
DummyRegressor makes predictions that ignore the input features.
This regressor serves as a simple baseline to compare against other more complex regressors. The specific behavior of the baseline is selected with the strategy parameter.
All strategies make predictions that ignore the input feature values passed as the X argument to fit and predict. The predictions, however, typically depend on values observed in the y parameter passed to fit.
Function-identical to sklearn.dummy.DummyRegressor, which is called inside.
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)