Is it ever correct to train a Ridge Regression model on the test set?
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I was reading an ML book on Ridge Regression and came across the following code:
rr = Ridge(alpha=0.5)
rr.fit(w_test, h_test)
rr.score(w_test, h_test)
The book explicitly mentions fitting the Ridge model on the test data.
My understanding is that a model should always be trained using the training set
(fit(X_train, y_train))
and evaluated on the test set
(score(X_test, y_test) or predict(X_test)).
Am I missing some context here, or is this simply an error in the book? I'd love to hear how experienced ML practitioners interpret this example.
u/Timely-Alternative53 — 2 days ago