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VarianceThreshold

Converter
DashAI.back.converters.scikit_learn.VarianceThreshold

Remove features whose variance across the training set is below a threshold.

For each feature column the sample variance is computed during fitting::

Var(x) = E[x^2] - (E[x])^2

Feature columns for which Var(x) < threshold are removed. Because the criterion is purely based on the marginal variance of each feature, this selector requires no class labels and runs in O(n * p) time.

Common use cases include:

  • Constant-feature removal — with the default threshold=0.0 any feature that takes the same value in every training sample is dropped.
  • Near-constant-feature removal — for binary features, a threshold of p * (1 - p) drops features that are True in fewer than a fraction p of samples (e.g. threshold=0.8 * 0.2 = 0.16 removes features that are True in less than 20 % of samples).
  • Pre-filtering before expensive selectors — quickly reducing dimensionality before applying supervised selection methods such as SelectKBest or RFECV.

References​

Parameters​

threshold : number, default=0.0
Features with a variance lower than this threshold will be removed.

Methods​

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

Defined on VarianceThreshold

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.