What are the key differences between automated ML and traditional ML?

What are the key differences between automated ML and traditional ML?

Automated ML (AutoML) simplifies the process of applying machine learning by automating tasks like feature selection and model tuning, while traditional ML requires more manual intervention and expertise.

Can you give me examples of when to use each?

Sure! Use AutoML for quick prototyping or when you lack deep ML expertise. Traditional ML is better for complex problems requiring custom solutions.

What are the advantages of using automated ML?

The advantages of AutoML include faster model development, reduced need for specialized knowledge, and the ability to handle large datasets efficiently.

Are there any limitations to automated ML?

Yes, limitations include less control over the modeling process, potential overfitting, and challenges in understanding the model's decisions.

Which one is more accurate?

Accuracy can vary based on the problem and data. Sometimes AutoML can match or exceed traditional methods, but traditional ML may outperform in specialized cases.