AWS Certified Machine Learning – Specialty — Question 211

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?

Answer options

• A. Pick a date so that 80% of the data points precede the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
• B. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
• C. Starting from the earliest date in the dataset, pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.
• D. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.

Correct answer: A

Explanation

The correct answer, A, involves selecting a date that ensures 80% of the historical data precedes it, which is crucial for time series forecasting to maintain temporal integrity. Option B incorrectly suggests that data points after the date should be used for training, breaking the chronological order. Options C and D do not respect the temporal aspect of the data, making them unsuitable for forecasting tasks.