Exam Professional Machine Learning Engineer topic 1 question 184 discussion

Since the question is to predict the for a new store and not sales prediction in general, the answer has to be 'A'

Time-Series Data: Your sales data has timestamps, indicating it's time-series data. A chronological split considers the order of the timestamps, ensuring the model is trained on historical trends. Predicting for New Store: Since you want to predict sales for a new store, a chronological split is better than a random split (option D) which wouldn't prioritize recent trends. Vertex AI Functionality: Vertex AI's chronological split functionality is specifically designed for time-series data and leverages the timestamp feature you provide to separate data for training, validation, and testing.

My answer C: A: Not Correct:  Splitting based on store name wouldn't guarantee temporal separation of data. Furthermore, for this problem is note to assign one store for each set, because the target is for a new store. B: Not Correct:  Randomly choosing data points across different time periods could lead to the model not capturing seasonal trends or temporal patterns effectively. C: CORRECT: it leverages the chronological nature of the data. Since the dataset contains sales data over time from different stores, using a chronological split ensures that the model is trained on data from earlier time periods and validated/tested on more recent data. D: Not Correct: Similar to B, a custom random split wouldn't ensure temporal separation and could lead to issues with capturing temporal trends.

I agree with b1a8fae

https://cloud.google.com/automl-tables/docs/data-best-practices#time

Anything different than option C could potentially lead to data leakage imo.

By using a manual split based on store names, you can train a model that is more sensitive to the unique characteristics of each store, ultimately leading to better predictions for the new store.

I say C , time-based splitting is always suggest