CERTIFIED-MACHINE-LEARNING-PROFESSIONAL · Question #53
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 rev
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Question
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?
Options
- Amlflow.load_model(model_uri)
- Bclient.list_artifacts(run_id)["feature-importances.csv"]
- Cmlflow.sklearn.load_model(model_uri)
- DThis can only be viewed in the MLflow Experiments UI
- Eclient.pyfunc.load_model(model_uri)
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