Metadata-Version: 2.2
Name: mlpack
Version: 4.6.0
Summary: a flexible, fast machine learning library
Home-page: http://www.mlpack.org/
Author: mlpack developers
Author-email: mlpack@lists.mlpack.org
License: BSD
Project-URL: Documentation, http://www.mlpack.org/doc/mlpack-4.6.0/python.html
Project-URL: Source, https://github.com/mlpack/mlpack/
Project-URL: Tracker, https://github.com/mlpack/mlpack/issues
Keywords: machine learning,data mining,deep learning,optimization
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
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mlpack is a fast, flexible machine learning library, written in C++, that aims
to provide fast, extensible implementations of cutting-edge machine learning
algorithms.  mlpack provides these algorithms as standalone Python functions,
which wrap the fast C++ implementations of the algorithms.

mlpack's techniques fall into a handful of categories:

 * _Classification_: logistic regression, perceptrons, random forests, linear
   SVMs, AdaBoost, etc.

 * _Regression_: linear regression, least angle regression, etc.

 * _Clustering_: Gaussian mixture models, k-means, mean shift, DBSCAN, etc.

 * _Geometry_: k-nearest-neighbor search, max-kernel search, locality sensitive
   hashing (LSH), etc.

 * _Preprocessing_: dataset splitting, binarization, scaling, one hot encoding,
   etc.

 * _Misc. / Other_: collaborative filtering, density estimation trees, Hidden
   Markov Models, kernel density estimation (KDE), etc.

 * _Transformations_: kernel PCA, sparse coding, large margin nearest neighbors,
   PCA, etc.

For more documentation on each individual function that mlpack provides, see the
[Python binding
documentation](https://www.mlpack.org/doc/user/bindings/python.html).
