Topic 1 Question 73
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?
Use then TFX ModelValidator tools to specify performance metrics for production readiness.
Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data.
Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
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コメント(17)
- 正解だと思う選択肢: C
it's seem C for me B is wrong cuz "Many machine learning techniques don’t work well here due to the sequential nature and temporal correlation of time series. For example, k-fold cross validation can cause data leakage; models need to be retrained to generate new forecasts"
👍 6hiromi2022/12/18 - 👍 6John_Pongthorn2023/01/25
- 正解だと思う選択肢: A
A. Use the TFX ModelValidator tools to specify performance metrics for production readiness.
TensorFlow Extended (TFX) ModelValidator is a useful tool for evaluating your model's performance on specific subsets of data before pushing it to production. It enables you to set specific performance metrics that the model should meet to be considered production-ready. ModelValidator can help ensure that your model is performing well on the different subsets of data that matter to your business, addressing the highly dynamic nature of customer behavior in the footwear retail context.
Option C, using the last relevant week of data as a validation set, may not be sufficient to validate the model's performance on various subsets of data.
👍 4alejandroverger2023/03/28
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