0ee11dac77
Following classes are added to support dataframe in java binding: - `Column` is an abstract type for a single column in tabular data. - `ColumnBatch` is an abstract type for dataframe. - `CuDFColumn` is an implementaiton of `Column` that consume cuDF column - `CudfColumnBatch` is an implementation of `ColumnBatch` that consumes cuDF dataframe. - `DeviceQuantileDMatrix` is the interface for quantized data. The Java implementation mimics the Python interface and uses `__cuda_array_interface__` protocol for memory indexing. One difference is on JVM package, the data batch is staged on the host as java iterators cannot be reset. Co-authored-by: jiamingy <jm.yuan@outlook.com>
eXtreme Gradient Boosting
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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
License
© Contributors, 2021. Licensed under an Apache-2 license.
Contribute to XGBoost
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
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