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Question 213 - MLS-C01 discussion

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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.
Answers
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.
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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.
Answers
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.
Answers
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.
Suggested answer: B

Explanation:

The best way to visualize the model's degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers. A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model's performance.References:

Histogram - Wikipedia

Model error - Wikipedia

SageMaker Model Monitor - visualizing monitoring results

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asked 16/09/2024
JAVIER MARDOMINGO SALAZAR
34 questions
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