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Which practice prevents train-test contamination during preprocessing?

A Splitting the data first and fitting scalers and encoders on the training split only
B Scaling the whole dataset before splitting
C Using the same random seed
D Normalising the target variable

Answer

Splitting the data first and fitting scalers and encoders on the training split only

Using a scikit-learn Pipeline fits preprocessing inside each cross-validation fold automatically, eliminating this class of error structurally.

All Machine Learning MCQs

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