Generate risk

Create prediction function

Refer to Prediction Section on to how to create a prediction function and add it to a version of a model.

Create validation dataset

PureML expects the dataset version to be used for validation in a specific manner.

Run your first evaluation

from pureml.decorators import dataset

@dataset("<dataset_name>")
def create_validation_dataset():
    x_test = #Data for testing
    y_test = #Labels for testing

    return {"x_test":x_test, "y_test":y_test}

Any dataset that is intended to be used in model validation should be registered in a dictionary format with mandatory keys; “x_test” for testing data, and “y_test” for dataset labels.

Running Evaluator

import pureml

pureml.eval(task_type='classification',
            label_dataset='<dataset_name:dataset_version>',
            label_model='<model_name:model_version>')

PureML supports two task types for evaluation, “classification”, and “regression”.