Databricks Certified Machine Learning - Associate
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In the context of AutoML, explain the role of model interpretability and how AutoML can help in understanding the decision-making process of the models it generates. Provide a detailed explanation of the techniques used by AutoML for model interpretability and their significance in improving trust and transparency in machine learning models.
A
AutoML does not provide any support for model interpretability, as it focuses solely on model performance.
B
AutoML provides limited support for model interpretability by generating a summary of the model's features and their importance.
C
AutoML supports model interpretability by using techniques such as feature importance ranking, partial dependence plots, and SHAP values to explain the decision-making process of the models it generates.
D
AutoML supports model interpretability by providing a detailed breakdown of the model's architecture and hyperparameters, but not by explaining the decision-making process.