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StandardScaler

Converter
DashAI.back.converters.scikit_learn.StandardScaler

Standardize features by removing the mean and scaling to unit variance.

For each feature column the transformation (z-score normalization) is::

x_scaled = (x - mean) / std

where mean and std are estimated from the training data. The result has zero mean and unit standard deviation. Centering and scaling can be disabled independently via with_mean and with_std.

Standardization is the most common preprocessing step for algorithms that assume normally distributed or zero-mean inputs, including SVMs, logistic regression, linear regression with regularization, principal component analysis, and most neural networks. Unlike MinMaxScaler, it is robust to differences in feature range but not to extreme outliers (because the standard deviation is influenced by them).

References​

Parameters​

use_copy : boolean, default=True
If False, try to avoid a copy and do inplace scaling instead.
with_mean : boolean, default=True
If True, center the data before scaling.
with_std : boolean, default=True
If True, scale the data to unit variance (or equivalently, unit standard deviation).

Methods​

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

Defined on StandardScaler

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.