> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.

# predict_clustering

> Use a pretrained clustering model to predict new data. 

Usually employed after the `train_clustering` step.
???+ info "Prediction Model"
To use this step successfully you need to make sure the dataset you're predicting on is
as similar as possible to the one the model was trained on. We check that the necessary data
types and columns are present, but you should pay attention to how you handled these in the
recipe the model was generated. Any changes might lead to a significant degradation in
model performance.

## Usage

The following example shows how the step can be used in a recipe.

<Accordion title="Examples" icon="code" defaultOpen="true">
  <Tabs>
    <Tab title="Example 1">
      ```stan theme={null}
      predict_clustering(ds, model) -> (data.predicted)
      ```
    </Tab>

    <Tab title="Signature">
      General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.

      ```stan theme={null}
      predict_clustering(ds: dataset, model: model_clustering[ds]
      ) -> (predicted: category)
      ```
    </Tab>
  </Tabs>
</Accordion>

## Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally
columns (`ds.first_name`), datasets (`ds` or `ds[["first_name", "last_name"]]`) or models (referenced
by name e.g. `"churn-clf"`).

<Accordion title="Inputs" icon="right-to-bracket">
  <ParamField path="ds" type="dataset" required>
    Contains the target column and the rest of the columns you wish to use in the model.
  </ParamField>

  <ParamField path="model" type="file[model_clustering[ds]]" required>
    File containing the model used to make the prediction.
  </ParamField>
</Accordion>

<Accordion title="Outputs" icon="right-from-bracket">
  <ParamField path="predicted" type="column[category]" required>
    Column containing the model predictions.
  </ParamField>
</Accordion>

## Configuration

The following parameters can be used to configure the behaviour of the step by including them in
a json object as the last "input" to the step, i.e. `step(..., {"param": "value", ...}) -> (output)`.

<Accordion title="Parameters" defaultOpen="true" icon="sliders">
  This step doesn't expect any configuration.
</Accordion>