Given the scenario, which of the following approaches is the MOST LIKELY to improve the model’s performance?

You are working as a data scientist at a financial services company tasked with developing a credit risk prediction model. After experimenting with several models, including logistic regression, decision trees, and support vector machines, you find that none of the models individually achieves the desired level of accuracy and robustness. Your goal is to improve overall model performance by combining these models in a way that leverages their strengths while minimizing their weaknesses.

Given the scenario, which of the following approaches is the MOST LIKELY to improve the model’s performance?
A . Apply stacking, where the predictions from logistic regression, decision trees, and support vector machines are used as inputs to a meta-model, such as a random forest, to make the final prediction
B . Implement boosting by training sequentially different types of models – logistic regression, decision trees, and support vector machines – where each new model corrects the errors of the previous ones
C . Use a simple voting ensemble, where the final prediction is based on the majority vote from the logistic regression, decision tree, and support vector machine models
D . Use bagging, where different types of models – logistic regression, decision trees, and support vector machines – are trained on different subsets of the data, and their predictions are averaged to
produce the final result

View Answer

Answer: A

Explanation:

Correct option:

Apply stacking, where the predictions from logistic regression, decision trees, and support vector machines are used as inputs to a meta-model, such as a random forest, to make the final prediction

via –

https://aws.amazon.com/blogs/machine-learning/efficiently-train-tune-and-deploy-custom-ensembles-usi

ng-amazon-sagemaker/

In bagging, data scientists improve the accuracy of weak learners by training several of them at once on multiple datasets. In contrast, boosting trains weak learners one after another.

Stacking involves training a meta-model on the predictions of several base models. This approach can significantly improve performance because the meta-model learns to leverage the strengths of each base model while compensating for their weaknesses.

For the given use case, leveraging a meta-model like a random forest can help capture the relationships between the predictions of logistic regression, decision trees, and support vector machines.

Incorrect options:

Use a simple voting ensemble, where the final prediction is based on the majority vote from the logistic regression, decision tree, and support vector machine models – A voting ensemble is a straightforward way to combine models, and it can improve performance. However, it typically does not capture the complex interactions between models as effectively as stacking.

Implement boosting by training sequentially different types of models – logistic regression, decision trees, and support vector machines – where each new model corrects the errors of the previous ones – Boosting is a powerful technique for improving model performance by training models sequentially, where each model focuses on correcting the errors of the previous one. However, it typically involves the same base model, such as decision trees (e.g., XGBoost), rather than combining different types of models.

Use bagging, where different types of models – logistic regression, decision trees, and support vector machines – are trained on different subsets of the data, and their predictions are averaged to produce the final result – Bagging, like boosting, is effective for reducing variance and improving the stability of models, particularly for high-variance models like decision trees. However, it usually involves training multiple instances of the same model type (e.g., decision trees in random forests) rather than combining different types of models.

References:

https://aws.amazon.com/blogs/machine-learning/efficiently-train-tune-and-deploy-custom-ensembles-usi ng-amazon-sagemaker/

https://aws.amazon.com/what-is/boosting/