MLS-C01 · Question #237
A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the datase
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Question
A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset. How should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?
Options
- APick a date so that 80% of the data points precede the date. Assign that group of data points as
- BPick a date so that 80% of the data points occur after the date. Assign that group of data points
- CStarting from the earliest date in the dataset, pick eight data points for the training dataset and
- DSample data points randomly without replacement so that 80% of the data points are in the
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