Page Reader AI | Exam Professional Machine Learning Engineer topic 1 question 184 discussion

Exam Professional Machine Learning Engineer topic 1 question 184 discussion - ExamTopics

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Problem Description

A retail company aims to build a sales prediction model using data from three existing stores to forecast sales for a new store. The dataset resides in Vertex AI and includes features like store name and sale timestamp.

Data Splitting Methods

The question focuses on the optimal method for splitting the data into training, validation, and testing sets. Several approaches are presented:

A. Manual Split: Assigning stores to each set.
B. Default Split: Using Vertex AI's default splitting mechanism.
C. Chronological Split: Using the sales timestamp to split the data sequentially.
D. Random Split: Randomly assigning data points to training (70%), validation (10%), and testing (20%) sets.

Suggested Solution

The suggested answer is C. Chronological split, using the sales timestamp as the time variable.

You work for a retail company. You have a managed tabular dataset in Vertex AI that contains sales data from three different stores. The dataset includes several features, such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon. You need to split the data between the training, validation, and test sets. What approach should you use to split the data?

A. Use Vertex AI manual split, using the store name feature to assign one store for each set
B. Use Vertex AI default data split
C. Use Vertex AI chronological split, and specify the sales timestamp feature as the time variable
D. Use Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set

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