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?

This is to track implementation of the ML-Features: https://spark.apache.org/docs/latest/ml-features

Bucketizer has been implemented in dotnet/spark#378 but there are more features that should be implemented.

• Feature Extractors
  • TF-IDF
  • Word2Vec (dotnet/spark#491)
  • CountVectorizer (https://github.com/dotnet/spark/p

Description

Use Case

Currently delta-rs Rust core depends on cli related dependencies like clap, which is unnecessary for downstream consumers. We should move delta-inspect code into its own crate within the same workspace like what we did for aws/delta-checkpoint so we can move all cli related dependencies to the cli crate and keep delta-rs dependency tree clean.

**Related Issu

Hi,

We’re running into an odd issue with our Development Account deployment regarding the instance pools. We have terraform modules for our databricks workspaces and all resources inside of them. We have folders for the clusters, instance-pools, cluster policies etc which when you drop json config files inside of them they will deploy/update what is needed.

All of our environments (6 total)

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