C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\basic.py:1555: UserWarning: categorical_feature in Dataset is overridden.
New categorical_feature is ['C01', 'C02', 'C03', 'C04', 'C05', 'C06', 'C07', 'C08', 'C09', 'C10', 'C11', 'C12', 'C13', 'C14', 'C15', 'C16', 'C17', 'C18', 'C19', 'C20', 'C21', 'C22', 'C23', 'C24', 'C25', 'C26']
  'New categorical_feature is {}'.format(sorted(list(cate

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

Support Series.median()

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

There can be a situation when all features are dropped during feature selection. Need to handle it. Maybe by throwing exception or raising a warning.

Code to reproduce:

import numpy as np
from supervised import AutoML

X = np.random.uniform(size=(1000, 31))
y = np.random.randint(0, 2, size=(1000,))

automl = AutoML(
    algorithms=["CatBoost", "Xgboost", "LightGBM"],
    model_t