with mlflow.start_run(run_name='untuned_random_forest'):
  n_estimators = 10
  model = RandomForestClassifier(n_estimators=n_estimators, random_state=np.random.RandomState(123))
  model.fit(X_train, y_train)
  predictions_test = model.predict_proba(X_test)[:,1]
  auc_score = roc_auc_score(y_test, predictions_test)
  mlflow.log_param('n_estimators', n_estimators)
  mlflow.log_metric('auc', auc_score)
  wrappedModel = SklearnModelWrapper(model)
  signature = infer_signature(X_train, wrappedModel.predict(None, X_train))
  mlflow.pyfunc.log_model("random_forest_model", python_model=wrappedModel, signature=signature)