Databricks Certified Machine Learning - Associate
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Consider a scenario where you are using AutoML to build a time series forecasting model, and you want to evaluate the model's performance. Explain the evaluation metrics that can be used to assess the performance of a time series forecasting model in AutoML, and provide a detailed explanation of each metric and its significance.
A
AutoML can only use the mean squared error (MSE) metric to evaluate the performance of a time series forecasting model.
B
AutoML can use a variety of evaluation metrics to assess the performance of a time series forecasting model, such as mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared.
C
AutoML can only use the mean absolute percentage error (MAPE) metric to evaluate the performance of a time series forecasting model.
D
AutoML cannot use any evaluation metrics to assess the performance of a time series forecasting model, as it relies solely on the model's predictions to evaluate performance.