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MinMaxScaler

Converter
DashAI.back.converters.scikit_learn.MinMaxScaler

Scale each feature to a fixed range, by default [0, 1].

For each feature column the transformation is::

x_scaled = (x - x_min) / (x_max - x_min) * (max - min) + min

where x_min and x_max are the per-feature minimum and maximum observed during fitting, and min/max are the bounds of the configured feature_range.

Unlike StandardScaler, this scaler is sensitive to outliers because the range is anchored to the observed extremes. It is appropriate when the downstream model requires bounded inputs (e.g. neural networks with sigmoid activations, k-nearest neighbours, or image pixel values that must lie in [0, 1]).

References​

Parameters​

min_range : number, default=0
The minimum value of the range to scale the data to.
max_range : number, default=1
The maximum value of the range to scale the data to.
clip : boolean, default=False
Set to True to clip the data to the feature range.

Methods​

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

Defined on MinMaxScaler

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().

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.