ikka
ikka
> Thanks for the initiative! It would be nice if you could add it to the demo script as well, so we can listen to what it sounds like :)...
Hey @iver56, May I take a look at this?
hey @abtheo Yes, you're right. As indicated in the issue, the error is because of the use of object types, to solve the issue you need to convert the object...
@pplonski Isn't `AutoML` filling missing values using standard methods like mean, median, mode, etc?
@pplonski Yes, That should be it. Maybe before filling missing values with any methods, we can do `pd.to_numeric(, errors='coerce')` and ensure that all the points are in float dtype.I think...
@pplonski This is only possible If AutoML imputing missing values after encoding the categorical features. Is it?
@pplonski Ok, But we don't impute categorical values using `np.nanmedian()` , that is where the issue is coming from. So I think it will be okay to go with the...
I can work on this, But shouldn't we also add oversampling and undersampling before we add imbalance learn? It can be in the same module ,eg `handle_imbalance.py`? @pplonski
Sure @pplonski
@mikichan007 Can you share some key insights from your experiments here for discussion?