Google Professional Machine Learning Engineer Question 7
Single answerGoogle Cloud PlatformYour company operates an e-commerce platform, and you want to predict whether a user will complete a purchase based on features such as session duration, pages visited, and previous purchase history. Which BigQuery ML model type is the most appropriate for solving this problem?
- A
Linear regression
- B
Binary logistic regression
- C
K-means clustering
- D
Time series model
Show answer and explanation
Correct answer: B
Explanation
Binary logistic regression is the most appropriate BigQuery ML model for this scenario because the goal is to predict a binary outcome: whether or not a user will complete a purchase. This type of problem involves classifying a data point into one of two categories, which is the exact purpose of binary logistic regression. Other model types like linear regression, k-means clustering, and time series models are designed for different kinds of problems and are not suitable for this task.
- A. Incorrect.
Linear regression is used for predicting continuous numerical values, such as sales revenue or temperature. It is not appropriate for binary classification tasks like predicting whether a user will complete a purchase.
- B. Correct.
Binary logistic regression is used for binary classification tasks, where the outcome is one of two possible categories (e.g., purchase or no purchase). This makes it the most appropriate choice for the given scenario.
- C. Incorrect.
K-means clustering is an unsupervised learning algorithm used to group data into clusters. It is not suitable for supervised classification tasks like predicting a binary outcome.
- D. Incorrect.
Time series models are used for forecasting trends over time, such as predicting future stock prices or sales. This does not apply to the binary classification task in the scenario.