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    vigneshanandbala
    @vigneshanandbala
    Hey hi mam happy to be a part of this nice community
    PH03NIX
    @tarek_chalan_au_twitter
    Hi sole thank you for creating this community 🤝
    mani
    @neomatrix369
    Hello all!
    Thanks Sole for creating this room
    DeyDipankar
    @DeyDipankar
    Hello all.. Thanks for creating this room @solegalli
    Soledad Galli
    @solegalli
    Welcome! I am glad you are here :)
    G.Satish
    @aryansatish7
    hello all , @solegalli Thanks much for creating this, looking forward to contribute and learn a lot
    ShyamGurunath
    @ShyamGurunath
    Hi everyone, Feature engine is one of my favorite. @solegalli Really glad you've created this community.
    Soledad Galli
    @solegalli
    Hello everyone. I wanted to pick your brains regarding a new transformer that we are considering for Feature-engine. In short, the transformer will learn the existing variables (names and order) in the train set during the fit method. And during transform() it will look at the variables of the dataset, and if it has variables that were not present in the train set, it will drop it, and if the train set has variables that are not present in the test set, it will add it either with nan, or zeros or a user desired input. Here is the issue: solegalli/feature_engine#132
    my question is, would you find that transformer useful? It would be really helpful if you could add your thoughts to that PR, or here if it makes it easier. Thank you!
    ShyamGurunath
    @ShyamGurunath
    @solegalli This transformer is good.Most of the time the variables in the train set is not present in the test set.So to handle that, this will be very useful.
    Joe
    @joeanton719
    Hi @solegalli . I recently encountered an error when working with RareLabel Encoder. I posted the question on Stackoverflow: https://stackoverflow.com/questions/68847937/feature-engine-rarelabelencoder-valueerror-could-not-convert-string-to-float
    Would really appreciate anyone's help regarding this, thanks!
    Joe
    @joeanton719

    Hi @solegalli . I recently encountered an error when working with RareLabel Encoder. I posted the question on Stackoverflow: https://stackoverflow.com/questions/68847937/feature-engine-rarelabelencoder-valueerror-could-not-convert-string-to-float

    UPDATE: Managed to solve the issue. I have added the answer in the same stack over flow forum.

    Soledad Galli
    @solegalli
    glad you had it sorted @joeanton719 . Feature-engine transformers have the option to select the variables directly. No need to use the column transformer at all. I added that to your thread in stack overflow. Cheers
    jonathanhexner
    @jonathanhexner
    Hi,
    Very cool package. Question about DecisionTreeDiscretiser. It seems to return the features discretized according to the response values and not the features values... Is this the intention?
    for example for the houses prices data set used in the example, applying it to GrLivArea it returns values in the range up to 500000, which corresponds to the response output and not the feature value.
    Soledad Galli
    @solegalli
    yep. that is the indented result. Did you have something else in mind?
    jonathanhexner
    @jonathanhexner
    It seems a little counter intuitive to me, but it's possible it's just new to me.
    Is there any way to "decode" the feature value?
    I guess there is no completely intuitive way of encoding it. I was thinking of the range mean,
    similar to how the other discretizers do it.
    Soledad Galli
    @solegalli
    Not with feature-engine. But if you have a specific way of discretizing in mind, feel free to create an issue to add the functionality you are after. Make sure to include links and clear guidelines as to what the transformer should do, and what is the desired output. Also, check if that functionality is not requested already before creating the issue if poss :)