chore: import upstream snapshot with attribution

examples/evaluation/evaluate_with_function.py

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+import shap
+import xgboost
+from sklearn.model_selection import train_test_split
+
+import mlflow
+
+# Load the UCI Adult Dataset
+X, y = shap.datasets.adult()
+
+# Split the data into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
+
+# Fit an XGBoost binary classifier on the training data split
+model = xgboost.XGBClassifier().fit(X_train, y_train)
+
+# Build the Evaluation Dataset from the test set
+eval_data = X_test
+eval_data["label"] = y_test
+
+
+# Define a function that calls the model's predict method
+def fn(X):
+    return model.predict(X)
+
+
+with mlflow.start_run() as run:
+    # Evaluate the function without logging the model
+    result = mlflow.evaluate(
+        fn,
+        eval_data,
+        targets="label",
+        model_type="classifier",
+        evaluators=["default"],
+    )
+
+print(f"metrics:\n{result.metrics}")
+print(f"artifacts:\n{result.artifacts}")