SHOGUN alternatives and similar libraries
Based on the "Machine Learning" category.
Alternatively, view SHOGUN alternatives based on common mentions on social networks and blogs.
9.8 9.4 L1 SHOGUN VS xgboostScalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
9.2 9.4 L1 SHOGUN VS vowpal_wabbitVowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
8.8 9.9 SHOGUN VS catboostA fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
4.1 0.0 L5 SHOGUN VS FidoA lightweight C++ machine learning library for embedded electronics and robotics.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of SHOGUN or a related project?
The SHOGUN machine learning toolbox
Unified and efficient Machine Learning since 1999.
Develop branch build status:
Donate to Shogun via NumFocus:
- See [doc/readme/ABOUT.md](doc/readme/ABOUT.md) for a project description.
- See [doc/readme/INSTALL.md](doc/readme/INSTALL.md) for installation instructions.
- See [doc/readme/INTERFACES.md](doc/readme/INTERFACES.md) for calling Shogun from its interfaces.
- See [doc/readme/EXAMPLES.md](doc/readme/EXAMPLES.md) for details on creating API examples.
See [doc/readme/DEVELOPING.md](doc/readme/DEVELOPING.md) for how to hack Shogun.
See API examples for all interfaces.
See the wiki for extended developer information.
|Python||mature (no known problems)|
|Octave||mature (no known problems)|
|Java/Scala||stable (no known problems)|
|Ruby||stable (no known problems)|
|C#||stable (no known problems)|
|R||beta (most examples work, static calls unavailable)|
|Perl||pre-alpha (work in progress quality)|
|JS||pre-alpha (work in progress quality)|
See our website for examples in all languages.
Shogun is supported under GNU/Linux, MacOSX, FreeBSD, and Windows.
The following directories are found in the source distribution.
Note that some folders are submodules that can be checked out with
git submodule update --init.
- src - source code, separated into C++ source and interfaces
- doc - readmes (doc/readme, submodule), Jupyter notebooks, cookbook (API examples), licenses
- examples - example files for all interfaces
- data - data sets (submodule, required for examples)
- tests - unit tests and continuous integration of interface examples
- applications - applications of SHOGUN (outdated)
- benchmarks - speed benchmarks
- cmake - cmake build scripts
Shogun is distributed under [BSD 3-clause license](doc/license/LICENSE.md), with optional GPL3 components. See [doc/licenses](doc/license) for details.
*Note that all licence references and agreements mentioned in the SHOGUN README section above are relevant to that project's source code only.