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HoldoutSplitter

Splitter
DashAI.back.splitters.HoldoutSplitter

Splitter that creates train, test, and validation partitions for holdout evaluation.

This strategy is appropriate when a single representative split is sufficient for model selection or final assessment. It is commonly used for quick experiments, hyperparameter tuning, and production-ready evaluation where the computational cost of repeated cross-validation would be excessive.

It is especially useful for large datasets and for workflows that require a simple partitioning scheme with clear train/test/validation boundaries.

References​

Parameters​

train, default=0.6
Proportion of the dataset assigned to the training partition.
test, default=0.2
Proportion of the dataset assigned to the test partition.
validation, default=0.2
Proportion of the dataset assigned to the validation partition.
stratify : boolean, default=False
Whether to preserve the class distribution across the splits.
shuffle : boolean, default=True
Whether to shuffle the data before splitting it.
random_state : integer, default=42
Seed used to make the split reproducible when shuffle is enabled.

Methods​

split(self, x: 'DashAIDataset', y: 'DashAIDataset') -> 'Tuple[DatasetDict, DatasetDict, Dict[str, Any]]'

Defined on HoldoutSplitter

Split the input data into holdout partitions and return the resulting datasets.

Parameters

x : DashAIDataset
Input dataset to partition.
y : DashAIDataset
Target values associated with x.

Returns

tuple
A tuple containing the partitioned input and output datasets, along with the indices used for each split.

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

Defined on HoldoutSplitter

Generate lists with train, test and validation indexes.

Parameters

x: DashAIDataset
Input dataset to partition.
y: DashAIDataset
Target values associated with x.

Returns

tuple[List, List, List]
Lists of indices for the training, test, and validation partitions.

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_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.

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.