Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance?

You are working on a machine learning project for a financial services company, developing a model to predict credit risk. After deploying the initial version of the model using Amazon SageMaker, you find that its performance, measured by the AUC (Area Under the Curve), is not meeting the company’s accuracy

requirements. Your team has gathered more data and believes that the model can be further optimized. You are considering various methods to improve the model’s performance, including feature engineering, hyperparameter tuning, and trying different algorithms. However, given the limited time and computational resources, you need to prioritize the most impactful strategies.

Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance? (Select two)
A . Increase the size of the training dataset by incorporating synthetic data and then retrain the existing model
B . Perform hyperparameter tuning using Bayesian optimization and increase the number of trials to explore a broader search space
C . Switch to a more complex algorithm, such as deep learning, and use transfer learning to leverage pre-trained models
D . Use Amazon SageMaker Debugger to debug and improve model performance by addressing underlying problems such as overfitting, saturated activation functions, and vanishing gradients
E . Focus on feature engineering by creating

View Answer

Answer: D, E

Explanation:

Correct options:

Focus on feature engineering by creating domain-specific features and use SageMaker Clarify to evaluate feature importance

Feature engineering is one of the most effective ways to boost model performance, particularly in model with better signals for prediction. SageMaker Clarify can be used to evaluate feature importance, helping you identify the most impactful features and further refine the model.

via – https://aws.amazon.com/sagemaker/clarify/

Use Amazon SageMaker Debugger to debug and improve model performance by addressing underlying problems such as overfitting, saturated activation functions, and vanishing gradients

A machine learning (ML) training job can have problems such as overfitting, saturated activation functions, and vanishing gradients, which can compromise model performance.

SageMaker Debugger provides tools to debug training jobs and resolve such problems to improve the performance of your model. Debugger also offers tools to send alerts when training anomalies are found, take actions against the problems, and identify the root cause of them by visualizing collected metrics and tensors.

SageMaker Debugger:

via – https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html

Incorrect options:

Increase the size of the training dataset by incorporating synthetic data and then retrain the existing model – Increasing the size of the dataset with synthetic data can improve model performance, but it also introduces the risk of adding noise or bias if the synthetic data is not carefully generated. This approach may not guarantee a significant performance boost unless the original dataset was severely lacking in size.

Switch to a more complex algorithm, such as deep learning, and use transfer learning to leverage pre-trained models – Switching to a more complex algorithm or using transfer learning could improve performance, but it also increases the risk of overfitting, especially if the new algorithm is not well suited to the data. Additionally, deep learning models require more data and tuning, which may not be feasible given the time and resource constraints.

Perform hyperparameter tuning using Bayesian optimization and increase the number of trials to explore a broader search space – Hyperparameter tuning, especially using Bayesian optimization, can help optimize the model’s performance, but the gains might be marginal if the underlying features are not informative. It’s a valuable approach, but may not be the most impactful first step.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html

https://aws.amazon.com/sagemaker/clarify/