Topic 1 Question 286
A digital media company wants to build a customer churn prediction model by using tabular data. The model should clearly indicate whether a customer will stop using the company's services. The company wants to clean the data because the data contains some empty fields, duplicate values, and rare values.
Which solution will meet these requirements with the LEAST development effort?
Use SageMaker Canvas to automatically clean the data and to prepare a categorical model.
Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model.
Use SageMaker Canvas automatic data cleaning and preparation tools. Use the built-in SageMaker XGBoost algorithm to train a regression model.
Use SageMaker Data Wrangler to clean the data. Use the SageMaker Autopilot to train a regression model
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コメント(3)
B 1.Data Cleaning: SageMaker Data Wrangler is designed for data preparation tasks, including handling missing values, duplicates, and rare values. It provides a visual interface to clean and transform tabular data efficiently. This addresses the data cleaning requirements mentioned in the question.
2.Model Training: Using the built-in SageMaker XGBoost algorithm is a common and effective choice for classification tasks like customer churn prediction. XGBoost is a powerful and widely used algorithm for binary classification problems.
👍 4xiaoeason2023/12/15- 正解だと思う選択肢: B
B. Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model.
Explanation:
SageMaker Data Wrangler:
SageMaker Data Wrangler is designed for efficient data cleaning and preparation. It provides a visual interface that simplifies the process of cleaning tabular data, handling missing values, and addressing duplicate or rare values. Data Wrangler can generate the necessary preprocessing code automatically, reducing the development effort. SageMaker XGBoost (for Classification):
XGBoost is a popular and powerful algorithm for classification tasks, including customer churn prediction. SageMaker provides a built-in XGBoost algorithm, making it easy to train a classification model without the need for extensive coding.
👍 1aquanaveen2023/12/17 - 正解だと思う選択肢: A
See: https://aws.amazon.com/blogs/machine-learning/predicting-customer-churn-with-no-code-machine-learning-using-amazon-sagemaker-canvas/ Canvas also does no-code data cleaning and preparation. So, least development effort is Canvas.
👍 1taustin22023/12/23
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