LLMs are known for their tendencies to ‘hallucinate’ and produce erroneous outputs that are not grounded in the training data or based on misinterpretations of the input prompt. They are expensive to train and run, hard to audit and explain, and often provide inconsistent answers. Thankfully...
from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, X_test=X_test, y=y_train, task='classification') 기