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SelectFdr

Converter
DashAI.back.converters.scikit_learn.SelectFdr

Select features by controlling the expected False Discovery Rate (FDR).

SelectFdr applies the Benjamini-Hochberg procedure to the p-values produced by a univariate scoring function, retaining only those features whose (adjusted) p-value is at most alpha. The FDR criterion bounds the expected proportion of selected features that are actually uninformative, offering a less conservative rejection policy than Family-Wise Error control while still providing statistical guarantees.

This filter is well suited to high-dimensional settings (e.g. genomics, metabolomics) where many features are tested simultaneously and a small fraction of false positives among the selected set is acceptable in exchange for higher sensitivity.

Key properties:

  • Supervised: requires the target array y at fit time.
  • alpha is the target FDR level in [0, 1]; typical values are 0.05 or 0.10.
  • Less conservative than FWE (Bonferroni) correction: retains more features at the same nominal alpha when the number of tests is large.
  • The number of retained features is data-driven and not fixed in advance.

Wraps scikit-learn's SelectFdr.

References​

Parameters​

alpha : number, default=0.05
The highest uncorrected p-value for features to be kept.

Methods​

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

Defined on SelectFdr

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.