A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?
To restore a scikit-learn random forest model and access its feature_importances_ attribute, the appropriate function to use is mlflow.sklearn.load_model(model_uri). This function is specifically designed to load models built using scikit-learn.
The correct answer is C. mlflow.sklearn.load_model(model_uri)
C. mlflow.sklearn.load_model(model_uri) for two different case, write different code 1. sklean : mlflow.sklearn.load_model(model_uri) 2. xgboost : mlflow.xgboost.load_model(model_uri)
https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.load_model
This line of code loads the scikit-learn model from the specified model URI, allowing you to access its attributes such as feature_importances. Doc: https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.load_model
C is correct