MLA-C01 Question 146
Single answerYou are building a machine learning solution to predict customer churn for a subscription-based streaming platform. The dataset contains customer demographics, subscription details, and usage patterns. The target variable is binary, indicating whether a customer has churned (1) or not (0). You want to use an Amazon SageMaker built-in algorithm to train the model quickly with minimal preprocessing. Which algorithm is the most appropriate for this task?
- A
Linear Learner
- B
K-Means
- C
XGBoost
- D
BlazingText
Show answer and explanation
Correct answer: A
Explanation
The Linear Learner algorithm is the best choice for this scenario because it is a SageMaker built-in algorithm specifically designed for binary classification tasks. It provides optimized performance and requires minimal preprocessing, making it ideal for structured datasets with a binary target variable like customer churn.
- A. Correct.
Linear Learner is well-suited for binary classification problems like predicting customer churn. It is a built-in SageMaker algorithm optimized for both classification and regression tasks with minimal preprocessing required.
- B. Incorrect.
K-Means is a clustering algorithm and is not applicable to this supervised learning problem, as the task involves predicting a binary target variable.
- C. Incorrect.
XGBoost is a powerful algorithm for supervised learning tasks, including classification. However, it is not a SageMaker built-in algorithm; instead, it is available as a pre-configured container. The question specifies using a SageMaker built-in algorithm.
- D. Incorrect.
BlazingText is designed for natural language processing tasks like text classification and word embeddings. It is not suitable for structured data and binary classification tasks like customer churn prediction.