How you are using LightGBM?

LightGBM component: R package

Environment info

Operating System: macOS 10.14

C++ compiler version: gcc 8.1.0

CMake version: 3.17.3

R version: 4.0.2

LightGBM version or commit hash: https://github.com/microsoft/LightGBM/tree/c07644d1d71540204a9b56f26667e8180bd009e2

Reproducible example(s)

Thanks to @Laurae2 for sharing this with m

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.

Create gifs with folder structure after AutoML training and add them to the Readme

ES reference: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-percentile-aggregation.html

Pandas
Dataframe.quantile

Series.quantile

I am sure we can support q paramete