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SelectPercentile

Converter
DashAI.back.converters.scikit_learn.SelectPercentile

Select the top percentile of features by a univariate statistical test.

SelectPercentile applies the same univariate scoring approach as SelectKBest but expresses the number of features to retain as a percentage of all available features rather than as an absolute count. Each feature is scored independently against the target using a chosen statistical function, and the top percentile percent are kept.

This makes the selector robust to datasets with varying numbers of input features, since the number of retained features scales automatically with the input dimensionality. It is particularly convenient for grid search experiments where the feature set size may change across cross-validation folds or preprocessing stages.

Key properties:

  • Supervised: requires the target array y at fit time.
  • percentile is an integer in [1, 100]; setting it to 100 passes all features through unchanged.
  • Uses the same family of scoring functions as SelectKBest (f_classif, chi2, mutual_info_classif, etc.).
  • Feature ranking is univariate and does not capture interactions.

Wraps scikit-learn's SelectPercentile.

References​

Parameters​

percentile : integer, default=10
Percent of features to keep.

Methods​

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

Defined on SelectPercentile

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.