What are the advantages of using single models in machine learning?

What are the advantages of using single models in machine learning?

Single models in machine learning are often simpler, easier to interpret, and require less computational power compared to ensemble methods.

Can you give me some specific examples of single models?

Sure! Common examples include linear regression, decision trees, and support vector machines.

How do single models compare to ensemble models in terms of performance?

While ensemble models often achieve higher accuracy, single models can be more efficient and easier to deploy in real-world applications.

Are there situations where a single model is preferred over an ensemble?

Yes, single models are preferred when interpretability is crucial, or when computational resources are limited.

What are the limitations of using single models?

Single models may underperform on complex datasets and can be prone to overfitting if not properly regularized.