NORMALIZE COLUMNS IN THE DATASET¶
Normalizes one or more numeric columns in a dataset using either standard (z-score) or min-max scaling. Use this worker to bring features onto a common scale before machine-learning or statistical analysis steps.
When to use¶
Classification: process.
Tagged: column_transform, dataset, feature_scaling, minmax, normalize, standard_scaler, z-score.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Dataset | dataset_1 | dataset | — | Input dataset containing the columns to be normalized; accepts any tabular dataset object available in the workflow. | |
| Columns | columns | scalar | — | One or more column names from dataset_1 to normalize; leave blank to normalize all numeric columns. | |
| Normalization Type | normalization_type | scalar | 1norm | Normalization method to apply: ‘standard’ (z-score, zero mean / unit variance — more robust to outliers, recommended default) or ‘minmax’ (scales values to the [0, 1] range). |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| dataset_normalization_output_1 | dataset_normalization_output_1 | dataset | Output dataset identical in structure to the input, with the selected columns replaced by their normalized values. |
Disciplines¶
- ai_ml.preprocessing
- data.dataset.transform
Runnable example¶
A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_normalize
Auto-generated from transformation schema. Worker id: dataset_normalize. Schema hash: 67f91fda2ea3. Hand-curated docs in workerexamples/ override this page when present.