.. _auto_dataset_compute_shap:

*DATASET COMPUTE SHAP*
======================

Fits a surrogate model (currently linear) on the supplied dataset and computes SHAP (SHapley Additive exPlanations) feature-importance values for each input column. Use this worker to quantify how much each predictor contributes to the target response and to rank variable importance across a design space.

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

Classification: **process**.

Tagged: ``dataset``, ``explainability``, ``feature_importance``, ``linear_model``, ``sensitivity``, ``shap``, ``surrogate``.

Inputs
------

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   :widths: 20 20 20 20 20 20

   * - Label
     - ID
     - Type
     - Default
     - Required
     - Description
   * - Dataset
     - dataset_1
     - dataset
     - —
     - 
     - Input dataset (tabular) containing both predictor and response columns; must be a d3VIEW dataset object with at least the columns named in input_column and output_column.
   * - Input Column
     - input_column
     - scalar
     - —
     - 
     - One or more column names (multi-select, drawn from dataset_1) that act as model features / independent variables for the SHAP analysis.
   * - Output Column
     - output_column
     - scalar
     - —
     - 
     - Single column name (drawn from dataset_1) that represents the target / dependent variable the surrogate model is trained to predict.
   * - Model type
     - model_type
     - scalar
     - liearModel
     - 
     - Surrogate model family used before computing SHAP values; currently only 'linearModel' (polynomial linear regression) is supported — leave at default unless additional model types are added.
   * - Model Params
     - model_params
     - textarea
     - order=1
     - 
     - Key=value pairs (comma-separated, e.g. 'order=1') controlling model hyperparameters; 'order' sets the polynomial degree for the linear model — default 'order=1' gives a first-order linear fit.

Outputs
-------

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   * - Label
     - ID
     - Type
     - Description
   * - SHAP values
     - dataset_shap_1
     - dataset
     - Tabular dataset of SHAP values, one row per observation and one column per input feature, quantifying each feature's additive contribution to the model prediction.

Disciplines
-----------

- ai_ml.prognosis
- ai_ml.surrogate
- data.statistics
- design_exploration.sensitivity

Runnable example
----------------

A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: `/api/workflow/example?id=dataset_compute_shap <https://www.d3view.com/api/workflow/example?id=dataset_compute_shap>`_

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   Auto-generated from <code>transformation</code> schema. Worker id: <code>dataset_compute_shap</code>. Schema hash: <code>fb8974b938d8</code>. Hand-curated docs in <code>workerexamples/</code> override this page when present.
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