Given the need for both high accuracy and the ability to handle imbalanced data, which SageMaker built-in algorithm is the MOST SUITABLE for this use case?

You are a data scientist at a financial technology company developing a fraud detection system. The system needs to identify fraudulent transactions in real-time based on patterns in transaction data, including amounts, locations, times, and account histories. The dataset is large and highly imbalanced, with only a small percentage of transactions labeled as fraudulent. Your team has access to Amazon SageMaker and is considering various built-in algorithms to build the model.

Given the need for both high accuracy and the ability to handle imbalanced data, which SageMaker built-in algorithm is the MOST SUITABLE for this use case?
A . Implement the K-Nearest Neighbors (k-NN) algorithm to classify transactions based on similarity to known fraudulent cases
B . Apply the XGBoost algorithm with a custom objective function to optimize for precision and recall
C . Select the Random Cut Forest (RCF) algorithm for its ability to detect anomalies in transaction data
D . Use the Linear Learner algorithm with weighted classification to address the class imbalance

View Answer

Answer: B

Explanation:

Correct option:

Apply the XGBoost algorithm with a custom objective function to optimize for precision and recall

The XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that tries to accurately predict a target variable by combining multiple estimates from a set of simpler models. The XGBoost algorithm performs well in machine learning competitions for the following reasons:

Its robust handling of a variety of data types, relationships, distributions.

The variety of hyperparameters that you can fine-tune.

XGBoost is a powerful gradient boosting algorithm that excels in structured data problems, such as fraud detection. It allows for custom objective functions, making it highly suitable for optimizing precision and recall, which are critical in imbalanced datasets. Additionally, XGBoost has built-in techniques for handling class imbalance, such as scale_pos_weight.

Incorrect options:

Use the Linear Learner algorithm with weighted classification to address the class imbalance –

The Linear Learner algorithm can handle classification tasks, and weighting classes can help with imbalance. However, it may not be as effective in capturing complex patterns in the data as more sophisticated algorithms like XGBoost.

Select the Random Cut Forest (RCF) algorithm for its ability to detect anomalies in transaction data – Random Cut Forest (RCF) is designed for anomaly detection, which can be relevant for fraud detection. However, RCF is unsupervised and may not leverage the labeled data effectively, leading to suboptimal results in a supervised classification task like this.

Implement the K-Nearest Neighbors (k-NN) algorithm to classify transactions based on similarity to known fraudulent cases – K-Nearest Neighbors (k-NN) can classify based on similarity, but it does not scale well with large datasets and may struggle with the high-dimensional, imbalanced nature of the data in this context.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html https://aws.amazon.com/blogs/gametech/fraud-detection-for-games-using-machine-learning/ https://d1.awsstatic.com/events/reinvent/2019/REPEAT_1_Build_a_fraud_detection_system_with_Amaz on_SageMaker_AIM359-R1.pdf