test_classification

Usually employed after the train_classification 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.

test_classification(ds: dataset, model: model_classification[ds], {
    "param": value,
    ...
}) -> (pred: column, prob: column, error: column, *split: column
)

target

string

required

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

positive_class

[string, null]

Name of the positive class. In binary classification, usually the class you’re most interested in, for example the label/class corresponding to successful lead conversion in a lead score model, the class corresponding to a customer who has churned in a churn prediction model, etc.

If provided, will return predicted probabilities for the positive class. If not provided, will return probabilities for the predicted class (i.e. the class with the highest probability).

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.

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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