Measures performance at each time step ahead.
This allows to measure the performance of a model at each time step along the horizon. A copy of the provided regression metric is made for each time step.
A regression metric.
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via
copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
Return the current performance along the horizon.
typing.List[float]: The current performance.
Update the metric at each step along the horizon.
- y_true (List[numbers.Number])
- y_pred (List[numbers.Number])