Google Professional Machine Learning Engineer Question 10
Single answerGoogle Cloud PlatformYou are working for a retail company that wants to predict whether a customer will make a purchase based on features such as age, income, browsing history, and past purchase behavior. You have decided to use BigQuery ML for this task. Which type of model should you build in BigQuery ML to address this problem?
- A
Linear regression
- B
Binary logistic regression
- C
K-means clustering
- D
Time-series forecasting
Show answer and explanation
Correct answer: B
Explanation
Since the task is to predict whether a customer will make a purchase (a binary outcome: yes or no), the most suitable model in BigQuery ML is binary logistic regression. This model is specifically designed for binary classification problems where the target variable has only two possible outcomes.
- A. Incorrect.
Linear regression is used for predicting continuous numeric values, such as sales revenue or temperature, and is not appropriate for a binary classification problem.
- B. Correct.
Binary logistic regression is the appropriate choice for a binary classification problem, where the goal is to predict one of two possible outcomes (e.g., purchase vs. no purchase).
- C. Incorrect.
K-means clustering is an unsupervised learning technique used for grouping data points into clusters, not for predicting binary outcomes.
- D. Incorrect.
Time-series forecasting is used for predicting future values based on historical time-based data, and it is not suitable for a binary classification problem.