Evaluate Python Class Design in Data Pipeline

Q: Evaluate Python Class Design in Data Pipeline

This question evaluates competency in Python class design for machine learning pipelines, focusing on the scikit-learn-style fit/transform interface, state management, reusability, prevention of data leakage, and performance considerations within the Machine Learning domain.

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Question

Scenario

You are reviewing a Python class used in an ML/data pipeline that follows the scikit-learn-style fit/transform pattern.

Assume a typical transformer interface: the class exposes fit(X, y=None) to learn parameters from training data and transform(X) to apply the learned transformation to new data. Optionally, it may implement fit_transform and be used inside a Pipeline.

Questions

At a high level, what does this class accomplish?
Why is the logic separated into fit() and transform() steps—what advantages does this design bring?
Point out any shortcomings or code smells you would look for in such a class.

Hints: Consider reusability, prevention of data leakage, state management, and performance.

Evaluate Python Class Design in Data Pipeline

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Evaluate Python Class Design in Data Pipeline

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