PerClassBreakdown
Precision, recall, F1 and support broken down per class.
The aggregate precision and recall metrics average over classes and so hide the case that matters most: a model that scores well overall while being useless on a small class. This table is that breakdown, with support included so a weak row can be read against how many samples back it.
References
Methods
compute(self, y_true, y_pred, class_names: Optional[List[str]] = None) -> List[DashAI.back.core.artifacts.Artifact]
PerClassBreakdownBuild the per class report table.
Parameters
- y_true : ndarray
- Encoded true class indexes.
- y_pred : ndarray
- Model predictions, probabilities or hard labels.
- class_names : Optional[List[str]]
- Class labels in encoded order.
Returns
- List[Artifact]
- A single table artifact, one row per class plus the averages.
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.