An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an…

The problem describes a supervised classification task (fraud detection) using tabular data with significant feature interdependencies and class imbalance. The LightGBM algorithm, a gradient boosting framework using tree-based models, is the most appropriate choice. Tree-based ensembles are highly effective at capturing complex, non-linear interactions between features, which is a key requirement ("many of the features have interdependencies"). The Amazon SageMaker implementation of LightGBM also provides specific hyperparameters, such as isunbalance or scaleposweight, to directly address the class imbalance problem, making it superior to other options for this specific scenario.

B. Linear learner: This algorithm assumes a linear relationship between features and the target and does not inherently capture complex feature interdependencies, which is a stated problem in the scenario.
C. К-means clustering: This is an unsupervised learning algorithm used for grouping data points. It is not suitable for a supervised classification task like fraud detection where labeled data is available.
D. Neural Topic Model (NTM): This is an unsupervised algorithm designed for topic modeling on text documents. It is completely irrelevant for a classification task on structured, tabular financial data.

1. LightGBM Algorithm Documentation: "LightGBM is a gradient boosting framework that uses tree-based learning algorithms... It is often used for regression and classification problems with tabular datasets... To handle an imbalanced dataset

use the isunbalance or scaleposweight parameter."

Source: AWS. (2023). Use the SageMaker LightGBM Algorithm. Amazon SageMaker Developer Guide. https://docs.aws.amazon.com/sagemaker/latest/dg/lightgbm.html

2. Linear Learner Algorithm Documentation: "The Amazon SageMaker Linear Learner algorithm provides a solution for both classification and regression problems." While it can handle classification

it does not inherently model the complex feature interactions that tree-based models like LightGBM excel at.

Source: AWS. (2023). Linear Learner Algorithm. Amazon SageMaker Developer Guide. https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html

3. Tree-based Models for Feature Interactions: Academic literature confirms that tree-based methods are adept at modeling interactions. "Decision trees can capture non-linear interaction effects between features... an advantage over parametric models like linear regression."

Source: Hastie

T.

Tibshirani

R.

& Friedman

J. (2009). The Elements of Statistical Learning: Data Mining

Inference

and Prediction. Springer. (Chapter 9.2.4

"Trees and Rules").