Topic 1 Question 282
A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?
Aggregate sales from stores in the same geographic area.
Apply smoothing to correct for seasonal variation.
Change the forecast frequency from daily to weekly.
Replace missing values in the dataset by using linear interpolation.
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- 正解だと思う選択肢: B
B. Apply smoothing to correct for seasonal variation.
Smoothing techniques, such as using moving averages or other time series smoothing methods, can help in reducing noise and capturing the underlying patterns in the sales data. Seasonal variation is a common issue in time series data, especially in retail where sales may exhibit regular patterns based on seasons, holidays, or other recurring events.
👍 2aquanaveen2023/12/17 - 正 解だと思う選択肢: D
Could be B or D. The question calls out that 10% of the data is missing, which a lot. Smoothing would help as well. I'll go with D.
👍 2taustin22023/12/23 Answer: D
👍 1Oralinux2023/12/29
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