.. _auto_dataset_encoder:

*DATASET ENCODER*
=================

Encodes categorical columns in a dataset using either label encoding (integer mapping) or one-hot / unique-column expansion. Use this worker to convert string or categorical features into numeric representations before feeding data into ML pipelines.

When to use
-----------

Classification: **process**.

Tagged: ``categorical``, ``dataset_transform``, ``encoder``, ``label_encoder``, ``one_hot``, ``preprocessing``.

Inputs
------

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   * - Label
     - ID
     - Type
     - Default
     - Required
     - Description
   * - Choose Dataset
     - dataset_1
     - dataset
     - —
     - 
     - Input dataset containing the columns to be encoded; leave empty only if the dataset will be injected dynamically by an upstream worker.
   * - Encoder Type
     - encoder_type
     - scalar
     - label
     - 
     - Encoding strategy to apply: 'label' assigns a unique integer to each category in-place, while 'unique_columns' expands each category into its own binary column (one-hot style); defaults to 'label'.
   * - Columns To Encode
     - columns
     - scalar
     - —
     - 
     - One or more column names from dataset_1 to encode; populated dynamically from the chosen dataset — leave blank to encode all detected categorical columns.
   * - Specify Encoding
     - mapper_input
     - encoder
     - —
     - 
     - Optional explicit value-to-encoding map per column (e.g. {"color": {"red": 0, "blue": 1}}); use this to enforce a fixed encoding scheme rather than deriving it automatically from the data.

Outputs
-------

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   * - Label
     - ID
     - Type
     - Description
   * - dataset_encoder_output_1
     - dataset_encoder_output_1
     - dataset
     - Transformed dataset identical in structure to the input but with the selected columns replaced (label mode) or expanded (unique_columns mode) with their numeric encodings.

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_encoder <https://www.d3view.com/api/workflow/example?id=dataset_encoder>`_

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   Auto-generated from <code>transformation</code> schema. Worker id: <code>dataset_encoder</code>. Schema hash: <code>177909fb4c03</code>. Hand-curated docs in <code>workerexamples/</code> override this page when present.
   </p>