For SageMaker's built-in classification algorithms like XGBoost, the input data must be numeric. The client identifier is a high-cardinality, non-informative feature that should be excluded to prevent overfitting and improve model performance. The target variable, operation status, must be converted from its original format (likely a string) into numeric labels (e.g., 0, 1) for the algorithm to process it as a classification target. This preprocessing ensures the data is in the correct format and structure for effective model training in SageMaker.

A. Using Amazon Comprehend is inappropriate for simple label encoding, and retaining the unique client identifier is a poor modeling practice.

B. Excluding the operation status field removes the target variable, which is essential for training a supervised learning model.

C. SageMaker's built-in algorithms like XGBoost require numeric data, not strings, for both features and the target label.

1. Amazon SageMaker Developer Guide: In the section "Input/Output Interface for the XGBoost Algorithm," the documentation specifies the data format requirements. For CSV format, it states, "The first column must contain the target variable... The data in the target column must be numeric." This supports encoding the operation status and implies that non-numeric, non-feature columns like identifiers must be handled.

Source: Amazon SageMaker Developer Guide, "XGBoost Algorithm," section "Input/Output Interface for the XGBoost Algorithm."

2. AWS Documentation - "Preparing Data for Amazon SageMaker": General machine learning best practices outlined by AWS emphasize the importance of feature engineering, which includes removing irrelevant or non-predictive columns like unique identifiers. These identifiers do not generalize and can negatively impact model training.

Source: Amazon SageMaker Developer Guide, "Prepare Data," subsection on "Feature Engineering."

3. Ng, A. (2023). CS229 Machine Learning Course Notes. Stanford University. In lecture notes covering supervised learning and practical advice for applying learning algorithms, it is a standard preprocessing step to remove features with no predictive value, such as unique ID numbers. It also details the necessity of converting categorical labels into a numerical representation for classification algorithms.

Source: Stanford University, CS229: Machine Learning, "Advice for applying machine learning" and "Classification" sections.