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SelectKBest

Converter
DashAI.back.converters.scikit_learn.SelectKBest

Select the K highest-scoring features using a univariate statistical test.

SelectKBest evaluates each input feature independently against the target variable using a scoring function (e.g. f_classif for ANOVA F-statistic, chi2 for chi-squared, or mutual_info_classif for mutual information), then retains the k features with the highest scores, discarding the rest.

This filter method is computationally cheap and can substantially reduce dimensionality before feeding data to a more expensive estimator. It is particularly useful as a first-pass feature selection step in classification and regression pipelines.

Key properties:

  • Supervised: requires the target array y at fit time.
  • Setting k='all' is a no-op that passes every feature through; useful for pipeline grid searches where k is a tuned parameter.
  • Feature ranking is based solely on univariate statistics; it does not account for feature interactions.
  • The choice of scoring function should match the problem type (classification vs. regression) and the scale of the features.

Wraps scikit-learn's SelectKBest.

References​

Parameters​

k, default=10
Number of top features to select.

Methods​

get_output_type(self, column_name: str = None) -> DashAI.back.types.dashai_data_type.DashAIDataType

Defined on SelectKBest

Return the DashAI data type produced by this converter for a column.

Parameters

column_name : str, optional
Not used; all output columns share the same type. Defaults to None.

Returns

DashAIDataType
A Float type backed by pyarrow.float64().

changes_row_count(self) -> 'bool'

Defined on BaseConverter

Indicate whether this converter changes the number of dataset rows.

Returns

bool
True if the converter may add or remove rows, False otherwise.

fit(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> DashAI.back.converters.base_converter.BaseConverter

Defined on SklearnWrapper

Fit the scikit-learn transformer to the data.

Parameters

x : DashAIDataset
The input dataset to fit the transformer on.
y : DashAIDataset, optional
Target values for supervised transformers. Defaults to None.

Returns

BaseConverter
The fitted transformer instance (self).

get_metadata(cls) -> 'Dict[str, Any]'

Defined on BaseConverter

Get metadata for the converter, used by the DashAI frontend.

Parameters

cls : type
The converter class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]
Dictionary containing display name, short description, image preview path, category, icon, color, and whether the converter is supervised.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

transform(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'DashAIDataset'

Defined on SklearnWrapper

Transform the data using the fitted scikit-learn transformer.

Parameters

x : DashAIDataset
The input dataset to transform.
y : DashAIDataset, optional
Not used. Present for API consistency. Defaults to None.

Returns

DashAIDataset
The transformed dataset with updated DashAI column types.

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.