We are looking to have n_features_out_ for transformers. This naming makes the difference explicit.
I would like to see some guidance on how an estimator implementation (e.g. in scikit-learn-contrib) is advised to maintain compatibility with Scikit-learn pre- and post- SLEP010. That is, we want to encourage developers to take advantage of super()._validate_data(X, y), but we also don't want to force them to set a minimal Scikit-learn >= 0.23 dependency (or do we?). What's the recommended way to do implement fit and predict in such an implementation? Is it to (a) not use _validate_data until the minimal dependency is reached? (b) implement a patched BaseEstimator in the library which inherits from Scikit-learn's BaseEstimator and adds _validate_data? (c) something else? Joel
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