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?

APIs

SparkSession

• python def getActiveSession(cls)
• scala def executeCommand(runner: String, command: String, options: Map[String, String]): DataFrame

DataFrame

• python def transform(self, func)
• python def tail(self, num)
  scala def tail(n: Int): Array[T]
• scala def printSchema(level: Int): Unit
• scala def explain(mode: String): U