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KFoldSplitter

Splitter
DashAI.back.splitters.KFoldSplitter

Splitter that generates K folds for cross-validation.

This strategy is a standard choice for estimating model performance when the data is not naturally grouped and the goal is to obtain several semi-independent evaluation partitions. It is widely used in supervised learning tasks such as tabular classification, regression, text processing, and image classification.

It is especially useful when a reliable estimate of generalization is needed without the additional complexity of group-aware or stratified schemes.

References​

Parameters​

n_splits : integer, default=5
Number of folds. Must be an integer greater than or equal to 2.
shuffle : boolean, default=True
Whether to shuffle the data before splitting it into folds.
random_state : integer, default=42
Seed used to make the split reproducible when shuffle is enabled.

Methods​

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

Defined on KFoldSplitter

Generate train/test index pairs for each 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 every fold.

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​