Focused crawls are collections of frequently-updated webcrawl data from narrow (as opposed to broad or wide) web crawls, often focused on a single domain or subdomain.
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?
Azure supports batching multiple delete operations in a single call. This can speed up vacuum.
We shuld add a new delete_objs implementation of StorageBackend for Azure.
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.
Scalable Data Science, course sets in big data Using Apache Spark over databricks and their mathematical, statistical and computational foundations using SageMath.
Books and Papers in Mathematics, Econometrics, Machine Learning, Finance etc for different levels that can be useful for Data Scientists, Developers and everyone whoo is interesting in STEM.
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?