Which of the following strategies is the MOST LIKELY to achieve an optimal balance between model performance, training time, and cost?

You are a machine learning engineer at a financial services company tasked with building a real-time fraud detection system. The model needs to be highly accurate to minimize false positives and false negatives. However, the company has a limited budget for cloud resources, and the model needs to be retrained frequently to adapt to new fraud patterns. You must carefully balance model performance, training time, and cost to meet these requirements.

Which of the following strategies is the MOST LIKELY to achieve an optimal balance between model performance, training time, and cost?
A . Deploy a simpler model like logistic regression to reduce training time and cost, while accepting a slight reduction in model accuracy
B . Implement a tree-based model like XGBoost with early stopping and hyperparameter tuning, balancing accuracy with reduced training time and computational cost
C . Use a deep neural network with multiple layers and complex architecture to maximize performance, even if it requires significant computational resources and longer training times
D . Choose a support vector machine (SVM) with a nonlinear kernel to enhance accuracy, regardless of
the increased training time and cost associated with large datasets

View Answer

Answer: B

Explanation:

Correct option:

Implement a tree-based model like XGBoost with early stopping and hyperparameter tuning, balancing accuracy with reduced training time and computational cost

XGBoost is known for its ability to deliver high performance with relatively efficient training times, especially with techniques like early stopping and hyperparameter tuning. This approach balances the need for accuracy with reduced computational cost and training time, making it an ideal choice for this scenario.

Incorrect options:

Use a deep neural network with multiple layers and complex architecture to maximize performance, even if it requires significant computational resources and longer training times – A deep neural network may provide high accuracy but typically requires significant computational resources and longer training times, leading to higher costs. This approach may not be feasible within a limited budget, especially with the need for frequent retraining.

Deploy a simpler model like logistic regression to reduce training time and cost, while accepting a slight reduction in model accuracy – Logistic regression is simple and cost-effective but may not achieve the level of accuracy required for a critical application like fraud detection. This tradeoff might be too significant if accuracy is compromised.

Choose a support vector machine (SVM) with a nonlinear kernel to enhance accuracy, regardless of the increased training time and cost associated with large datasets – SVMs with nonlinear kernels can be very accurate but are computationally intensive, particularly with large datasets. The increased training time and cost might outweigh the benefits, especially when there are more cost-effective alternatives like XGBoost.

Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html