Hi I'm have succesfully registered my model using the feature engineering client with the following codes:

with mlflow.start_run():
    # Calculate the ratio of negative class samples to positive class samples
    ratio = (len(y_train) - y_train.sum()) / y_train.sum()

    # Fit model
    xgb_model = xgb.XGBClassifier(scale_pos_weight=ratio)
    xgb_model.fit(X_train, y_train)

    fe.log_model(
      model=xgb_model,
      artifact_path=MODEL_NAME,
      flavor=mlflow.sklearn,
      training_set=training_set,
      registered_model_name=MODEL_NAME
    )

There are two questions:

1. Why is the model still shown as pyfunc in the model registry when the flavor I specified was mlflow.sklearn?

2.  Can I use the following codes for prediction:

model = mlflow.sklearn.load_model(model_version_uri)

# Predict with model
prob_pred = model.predict_proba(df)[:, 1]

or do I must use score_batch()? As I would need prediction to be probabilities instead of 1/0s.

Thanks!

#model_flavor #feature_store #score_batch #xgboost #sklearn