Louis Lacombe

Hey @vtaquet and @fjpa121197, we are currently creating a `cv="prefit"` method where you will be able to pre-train your models and then use those directly with `MapieQuantileRegressor()` ([PR#214](https://github.com/scikit-learn-contrib/MAPIE/pull/214)). For the...

Hey @cerlymarco, thank you! Indeed, this could be added. Could you give us a use case for this issue? Additionally, note that in the .fit() of `MapieRegressor` does both fitting...

Hi @cerlymarco, thank you for the suggestion. Indeed, this makes a lot of sense. For the moment, we are quite busy with other projects or more documentation of methods. If...

Hey @masakljun! It's important that for `MapieQuantileRegressor` you provide an estimator that can perform quantile regression. As such, I would suggest using `LGBMRegressor(objective='quantile', alpha=0.5)` or `GradientBoostingRegressor(loss="quantile)`. If you need to...

Hey @jfowkes @ragonneau, Any news/updates the topic? We are having a similar [issue](https://github.com/scikit-learn-contrib/MAPIE/pull/429/files) on our library! Thank you!

Hey @caeduft, thank you for your message. So indeed, this is something that could be fixed using a wrapper and naming the `refit` method `fit`. Please checkout issue #340 where...

We will close this PR as it will be taken care of in #468.

Hi @gmartinonQM, thank you for this information. If you want replicable results for the moment, I would suggest using the `random_state` in the `.fit` method. However, this is indeed something...

Hi @gmartinonQM, thank you for your issue. Indeed, this would be a good option to have a good conditional coverage. We plan to tackle this by implementing a conditional method...

Hey @gmartinonQM, After discussing it with the MAPIE team, we believe this would indeed be beneficial. This is something we will take into account for the next releases! Will let...