ForecastErrorByStep
Mean absolute error at each step of the horizon, validation and test.
One number per run hides how the error grows with distance: a model can be good one step out and useless six steps out. Each bar is the mean absolute error of the forecasts made that many steps past their origin, over the validation origins, beside the test's error at the same step. A staircase that climbs steeply says to forecast less far, or to choose a model that holds up over the horizon the run needs.
Methods
compute_forecasts(self, forecasts: Dict[str, Any]) -> List[DashAI.back.core.artifacts.Artifact]
ForecastErrorByStepDraw the mean absolute error per step, one bar series per partition.
compute(self, y_true, y_pred, class_names: Optional[List[str]] = None) -> List[DashAI.back.core.artifacts.Artifact]
ForecastingReportA forecasting report has no rows to draw: see compute_forecasts.
get_credential(self, name: str)
ConfigObjectResolve a registered credential component by name.
Parameters
- name : str
- Credential component class name (e.g. "HuggingFaceCredential").
Returns
- BaseCredential
- An instance of the requested credential component.
get_metadata(cls) -> Dict[str, Any]
BaseReportGet metadata values for the current report.
Returns
- Dict[str, Any]
- UI metadata, including whether the report needs a model that outputs class probabilities.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
validate_and_transform(self, raw_data: dict) -> dict
ConfigObjectIt takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.
Parameters
- raw_data : dict
- A dictionary with the data provided by the user to initialize the model.
Returns
- dict
- A validated dictionary with the necessary objects.