1  from sklearn.datasets import load_diabetes
 2  from sklearn.linear_model import LinearRegression
 3  from sklearn.model_selection import train_test_split
 4  
 5  import mlflow
 6  
 7  diabetes_dataset = load_diabetes()
 8  
 9  X_train, X_test, y_train, y_test = train_test_split(
10      diabetes_dataset.data, diabetes_dataset.target, test_size=0.33, random_state=42
11  )
12  
13  with mlflow.start_run() as run:
14      model = LinearRegression().fit(X_train, y_train)
15      model_info = mlflow.sklearn.log_model(model, name="model")
16  
17      result = mlflow.evaluate(
18          model_info.model_uri,
19          X_test,
20          targets=y_test,
21          model_type="regressor",
22          evaluators="default",
23          feature_names=diabetes_dataset.feature_names,
24          evaluator_config={"explainability_nsamples": 1000},
25      )
26  
27  print(f"metrics:\n{result.metrics}")
28  print(f"artifacts:\n{result.artifacts}")