What are the differences between automated ML and traditional ML?

What are the differences between automated ML and traditional ML?

Automated ML (AutoML) simplifies the process of applying machine learning by automating model selection, training, and tuning. Traditional ML requires more manual intervention and expertise in selecting algorithms and tuning parameters.

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 where domain knowledge is crucial.

What are the advantages of using automated ML?

Automated ML speeds up the model development process, reduces the need for extensive ML knowledge, and often leads to better performance through optimized models.

Are there any limitations to automated ML?

Yes, AutoML can struggle with highly specialized tasks, may not provide the same level of customization as traditional methods, and can be less transparent.

How do I get started with automated ML in Python?

You can start with libraries like Auto-sklearn, TPOT, or H2O.ai. They provide user-friendly interfaces for implementing AutoML in Python.