Evaluate
Create your first Evaluation
Evaluate
Create your first Evaluation
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:dataset_branch>")
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_branch:dataset_version>',
label_model='<model_name:model_branch:model_version>')
PureML supports two task types for evaluation, “classification”, and “regression”.