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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.

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Usage

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

Examples

  • Example 1
  • Signature
predict_clustering(ds, model) -> (data.predicted)

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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").
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ds
dataset
required
Contains the target column and the rest of the columns you wish to use in the model.
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model
file[model_clustering[ds]]
required
File containing the model used to make the prediction.
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predicted
column[category]
required
Column containing the model predictions.

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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).

Parameters

This step doesn’t expect any configuration.
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