Usually employed after the train_regression 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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target
string
required

Target variable. Name of the column that contains your target values (labels).

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split
[object, object]

Train/test split configuration. Identify the splits using an existing column or create a randomized split. In either case, the model will be refit on the train split and evaluated on the test split.

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refit
boolean

Whether to retrain the model. If set to true, the model will be refit on the train split before evaluation. If set to false, the model will be evaluated on the test split without refitting. If no split configuration is provided, this parameter is ignored.

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