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?
Scalable Data Science, course sets in big data Using Apache Spark over databricks and their mathematical, statistical and computational foundations using SageMath.
Some of the tests require global state mutation as part of the setup. It would be better to perform these actions in a global setup routine. This is not something that Rust/Cargo supports out of the box, so we need to do some research here.
Connect-Databricks.ps1 uses "https://login.microsoftonline.com" as part of the URI to connect. When retrieving a token for a non-AzureCloud tenant (e.g. AzureUSGovernment) the URI root would be different (e.g., "https://login.microsoftonline.us"). As such, cannot use this task to deploy to other tenant types. Would be helpful to be able to specify an Azure Environment and connect to the right e
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?