Topic 1 Question 118
2 つ選択A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters. Which changes to the CreatePredictor API call could improve the MAPE?
Set PerformAutoML to true.
Set ForecastHorizon to 4.
Set ForecastFrequency to W for weekly.
Set PerformHPO to true.
Set FeaturizationMethodName to filling.
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I would choose A and D, however both of them is not possible at the same time. The question is ambiguous, it could mean which two options, but no necessarily both. A - If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function, set PerformAutoML to true. D - The following algorithms support HPO: - > DeepAR+.
👍 17scuzzy20102021/10/22It is A and D, there are no weekly data, they have only monthly data and can not switch horizon to 4
👍 7Vita_Rasta844442021/10/25Why are not B and C? The question asks about modifications that increase MAPE (thats bad): B - If FH is larger, error will increase C - Data is based on months, change that will make erros on forecasting values E - There is no data gap so is useless A - Selec best between all should DECREASE MAPE D - Tunning hyperparms will DECREASE MAPE
👍 5vanluigi2022/05/15
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