Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 14 discussion

Answer is B

https://docs.aws.amazon.com/en_kr/sagemaker/latest/dg/lightgbm.html

Linear learner is a built-in algorithm where LightBM is not

ChatGPT say it's A: LightGBM

Is supported by Sagemaker

Linear Learner is a built-in algorithm provided by SageMaker for supervised learning tasks like regression and classification. LightGBM is not a built-in algorithm in Amazon SageMaker. While it is a strong gradient-boosting algorithm, it would need to be implemented as a custom script in SageMaker, which increases operational overhead.

https://docs.aws.amazon.com/en_kr/sagemaker/latest/dg/lightgbm.html

In an ideal scenario, for a problem with these characteristics (fraud detection, class imbalance, feature interdependencies, complex patterns), a tree-based ensemble method like XGBoost (which is a SageMaker built-in algorithm) would be more suitable. XGBoost can handle non-linear relationships, is robust to class imbalance with proper tuning, and can capture complex patterns in the data. However, given the options provided and the requirement to use a SageMaker built-in algorithm, the Linear learner is the best available choice among these options for this specific fraud detection task.

A. Light BGM : It's suitable model, but not built-in model for SageMaker. Answer B. Linear learner : suitable model, built-in model for SageMaker. C. K-means clustering : groups similar data points, not suitable for classification problems, and it's unsupervised learning algorithm so doesn't fit in this case(fraud detection). D. Neural Topic Model: used for topic modeling and document classification, not suitable for fraud detection

Here's why LightGBM is the most suitable algorithm for this fraud detection task: Handling Class Imbalance: LightGBM is particularly effective at handling imbalanced datasets, which is a key issue mentioned in the problem statement. It has built-in mechanisms to deal with class imbalance. Feature Interdependencies: LightGBM can capture complex feature interactions through its tree-based structure, addressing the issue of feature interdependencies mentioned in the problem. Capturing Underlying Patterns: As an advanced gradient boosting framework, LightGBM is excellent at capturing complex patterns in data, which the current algorithm is struggling with. Suitable for Fraud Detection: LightGBM is widely used in fraud detection tasks due to its high performance and ability to handle large datasets efficiently. Handling Various Data Types: It can work well with the mix of data types likely present in transaction logs, customer profiles, and database tables.

We have an unbalanced dataset, this means we have labelled dataset thus we are going to use a supervised model training. This reduce options to A and B (K-means and NTM are unsupervised). Both LightGBM and Linear Learner provides hyperparameter to manage unbalanced datasets, respectively "scale-pos_weight" and "positive_example_weight_mult". I would go for LightGBM as this algorithm is more suited to handle complex relationship among features, while Linear Learner learns a linear function, or, for classification problems, a linear threshold function, and maps a vector x to an approximation of the label y.

Linear Learner. LightGBM is NOT a built in algorithm which the question asks for.

This is a binary classification problem so LightGBM so be used. Other algorithms are not for binary classification.

Is LightGBM Built-in? Technically, no, LightGBM is not a “built-in algorithm” in the same category as SageMaker’s core algorithms (like Linear Learner or XGBoost). However, it is supported through prebuilt containers, which makes it easy to use in SageMaker.

A. LightGBM: Handles class imbalance; captures feature interdependencies; models complex patterns. B. Linear Learner: Limited with interdependent features; struggles with complex patterns; suitable for linear relationships. C. K-means Clustering: Unsupervised algorithm; not suitable for classification; can't handle class imbalance. D. Neural Topic Model (NTM): Designed for topic modeling; unsuitable for fraud detection; doesn't address class imbalance.

This is a binary classification problem so LightGBM so be used. Other algorithms are not for binary classification.

Light Gradient Boosting Machine is effective for handling class imbalances and feature interdependencies.