LeaveOneOutSplitter
Splitter that creates one fold per sample by leaving one example out at a time.
This exhaustive strategy is useful for very small datasets where every observation should be tested in turn and the computational cost remains acceptable. It is often used as a reference method in small-sample studies and in settings where a highly thorough estimate of performance is desired.
References
Methods
split_indexes(self, x: 'DashAIDataset', y: 'DashAIDataset') -> 'List[Tuple[List, List]]'
LeaveOneOutSplitterGenerate train/test index pairs following the leave-one-out scheme.
Parameters
- x : DashAIDataset
- Input dataset whose length determines the number of available samples.
- y : DashAIDataset
- Target values associated with
x. This argument is accepted for interface consistency but is not used directly by the splitter.
Returns
- list[tuple]
- A list of train/test index pairs, one for each sample in the dataset.
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'
FoldSplitterReturn metadata describing the splitter's compatibility.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
prepare_y(self, y)
BaseSplitterEncode the target variable for stratified splitting.
Parameters
- y : object
- Target values to encode. This may be a list, a pandas-like object, or a DashAI dataset that exposes a single target column.
Returns
- object
- Encoded labels suitable for stratified splitting.
split(self, x: 'DashAIDataset', y: 'DashAIDataset') -> 'Tuple[List[DatasetDict], List[DatasetDict], Dict[str, Any]]'
FoldSplitterCreate folds and return both the partitioned datasets and the indices.
Parameters
- x : DashAIDataset
- Input dataset to split.
- y : DashAIDataset
- Target values associated with
x.
Returns
- tuple[list, list, dict]
- A tuple containing the split datasets for every fold and a mapping from fold names to their corresponding train/test indices.
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.