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RepeatedKFoldSplitter

Splitter
DashAI.back.splitters.RepeatedKFoldSplitter

Splitter that repeats the K-fold procedure multiple times.

Repeating the folds helps reduce the variance of the performance estimate by averaging over several random resamplings of the same evaluation scheme. This is useful when a single K-fold run is too noisy or when more stable estimates are needed for model comparison.

References​

Parameters​

n_splits : integer, default=5
Number of folds. Must be an integer greater than or equal to 2.
n_repeats : integer, default=2
Number of times the K-Fold procedure is repeated. Must be an integer greater than or equal to 2.
random_state : integer, default=42
Seed used to make the repeated split reproducible.

Methods​

split_indexes(self, x: 'DashAIDataset', y: 'DashAIDataset') -> 'List[Tuple[List, List]]'

Defined on RepeatedKFoldSplitter

Generate train/test index pairs for each repetition of the K-fold split.

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 for all folds and repeats.

get_credential(self, name: str)

Defined on ConfigObject

Resolve 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'

Defined on FoldSplitter

Return metadata describing the splitter's compatibility.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

prepare_y(self, y)

Defined on BaseSplitter

Encode 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]]'

Defined on FoldSplitter

Create 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

Defined on ConfigObject

It 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.

Compatible with​