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scikit-learn is literally built with OOP in mind. It’s what provides consistency across all classifiers or regressors. It’s what allows you, the user, to be guaranteed to have fit and predict methods.

There are just so many things that are used by the community that are OOP driven but they don’t recognize. I mean, PyTorch’s object oriented architecture brought simplicity without compromising complexity at a time when TensorFlow was far to verbose and difficult to build with.

Both are paradigms with their use cases. Functional Programming is nice for pipelines and orchestrating tasks. OOP is great for building entities, like User or Product. It’s not one or the other. It’s both. Apply when necessary.

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