Google Professional Machine Learning Engineer Question 9
Single answerGoogle Cloud PlatformYou are a machine learning engineer tasked with building a model in BigQuery ML to predict whether a customer will purchase a product (yes or no) based on features such as age, income, and browsing history. Which type of BigQuery ML model should you create to solve this problem?
- A
LINEAR_REG
- B
LOGISTIC_REG
- C
KMEANS
- D
MATRIX_FACTORIZATION
Show answer and explanation
Correct answer: B
Explanation
LOGISTIC_REG is the appropriate BigQuery ML model for this task because it is specifically designed for binary classification problems, where the target variable has two possible outcomes. In this scenario, the goal is to predict whether a customer will purchase a product ('yes' or 'no'), which fits the binary classification framework. Other options like LINEAR_REG, KMEANS, and MATRIX_FACTORIZATION are designed for different types of machine learning tasks and are not suitable for this problem.
- A. Incorrect.
LINEAR_REG is used for regression tasks, which predict continuous numerical values, not binary outcomes like 'yes' or 'no'.
- B. Correct.
LOGISTIC_REG is the correct choice because it is designed for binary classification tasks, such as predicting two possible outcomes (e.g., yes/no or true/false).
- C. Incorrect.
KMEANS is used for clustering tasks, which group data into clusters based on similarities, but it is not suitable for supervised binary classification.
- D. Incorrect.
MATRIX_FACTORIZATION is used for recommendation systems and is not applicable for binary classification tasks.