Scikit-learn Pipelines vs Keras: Which Should I Use?

What are the main differences between Scikit-learn pipelines and Keras?

Scikit-learn pipelines are great for traditional machine learning workflows, allowing for easy preprocessing and model training. Keras, on the other hand, is designed for deep learning and provides a more flexible architecture for building neural networks.

When should I use Scikit-learn pipelines instead of Keras?

You should use Scikit-learn pipelines when working with structured data and traditional ML algorithms. Keras is better suited for tasks involving deep learning, such as image or text processing.

Can I integrate Keras models into Scikit-learn pipelines?

Yes, you can integrate Keras models into Scikit-learn pipelines using the KerasClassifier or KerasRegressor wrappers, allowing you to leverage the benefits of both frameworks.

What are some advantages of using Keras for deep learning?

Keras offers a user-friendly API, modularity, and the ability to easily experiment with different architectures, making it ideal for deep learning projects.