RFC / API add option to fit/predict without input validation

Goal

Ideally, this issue helps to sort out an API.

Describe the workflow you want to enable

I’d like to be able to switch on/off:

1. Parameter validation in fit
2. Input array validation in fit and predict
3. All other validation steps in fit and predict (e.g. check_is_fitted)

Something like

model = RandomForestRegressor(validate_params=False)
model.fit(validate_input=False)
model.predict(validated_input=False)

Note that some estimators like Lasso already support check_input in fit.

The main reason to do so is improved performance.

Additional context

Related issues and PRs are #16653, #20657, #21578 (in particular https://github.com/scikit-learn/scikit-learn/pull/21578#discussion_r745491121) Related discussions: https://github.com/scikit-learn/scikit-learn/discussions/21810

[lorentzenchr]

Mmh🤔 @glemaitre You have a good point, indeed. The context manager helps. My intuition still is to have it as configurable option in estimators. I would be very interested in the opinion and experience of others.

[ogrisel]

Also there is an impact with feature names:

If b) is adopted and a meta-estimator is fit on a dataframe, then feature names are extracted by the meta-estimator and then a numpy array without feature names is passed to the base estimators. But sometimes the feature names could be useful for the base estimator (e.g. to specify features to be treated as categorical variables for a HistGBRT model for instance).