A retail company uses a machine learning (ML) model for daily sales forecasting. The company’s brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company’s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model’s inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model’s degradation MOST accurately?
A. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
B. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
C. Create a line chart with the weekly mean absolute error (MAE) of the model.
D. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
