microsoft/SynapseML

[LightGBM] Regression: how to penalize negative predictions

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#918 opened on Sep 2, 2020

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 (18 comments) (0 reactions) (2 assignees)Scala (5,228 stars) (861 forks)batch import
area/lightgbmenhancementgood first issue

Description

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?

Thx in advance!

Contributor guide

Tech stack
scala
Domain
machine learning
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
blocked
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
LightGBM basicsCustom objective functions
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40
Research direction
Investigate LightGBM's documentation on custom objectives (https://lightgbm.readthedocs.io). Check the SynapseML source code for the LightGBM integration (likely in the 'lightgbm' folder). Look for any existing custom objective examples in the repository's issues or PRs. Engage with the assignees to understand if they have a proposed solution.