| Name : R-interpret
| |
| Version : 0.1.33
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.7
| Date : 2024-06-14 11:10:03
|
| Group : Development/Libraries/Other
| Source RPM : R-interpret-0.1.33-lp153.1.7.src.rpm
|
| Size : 0.51 MB
| |
| Packager : (none)
| |
| Summary : Fit Interpretable Machine Learning Models
|
Description :
Package for training interpretable machine learning models.
Historically, the most interpretable machine learning models were not
very accurate, and the most accurate models were not very
interpretable. Microsoft Research has developed an algorithm called the
Explainable Boosting Machine (EBM) which has both high accuracy and
interpretable characteristics. EBM uses machine learning techniques
like bagging and boosting to breathe new life into traditional GAMs
(Generalized Additive Models). This makes them as accurate as random
forests and gradient boosted trees, and also enhances their
intelligibility and editability. Details on the EBM algorithm can be
found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul
Koch, Marc Sturm, and Noemie Elhadad (2015,
< doi:10.1145/2783258.2788613>).
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
