MLS-C01 Question #237: Real Exam Question with Answer & Explanation

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

Explanation

For time series data, it is important to split the dataset chronologically, with the training dataset containing the earlier dates and the validation dataset containing the later dates.

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