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mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency.
Data Mining Software Free
•Python method for machine learning •Provides high-level functions and classes •Works with Python 2 and 3 •Open Source •Compatible with PyInstaller
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
•Python method for machine learning •Provides high-level functions and classes •Works with Python 2 and 3 •Open Source
What are the benefits?
• Provides a wide range of machine learning methods • Perform many statistical analyses • Easy to manipulate data • Open-source: free to download, use, extend • Access to online/PDF documentation
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mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3.
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Mlpy know as Machine Learning Python represents a python method for machine learning built on top of NumPy/SciPy (Python-based ecosystem of open-source software for mathematics, science, and engineering) and the GNU Scientific Libraries (represents numerical library for C and C++ programmers where a wide range of mathematical routines such as random number generators, special functions and least-squares fitting are provided).
Wide range of state-of-the-art machine learning methods are provided for supervised and unsupervised problems and mlpy is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. It provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, and clustering and feature selection.
This platform works with Python 2 and 3 and it is Open Source which means that it is distributed under the GNU General Public License version 3 and it is free. This platform has several features such as: regression that contains - least squares, ridge regression, last angle regression, elastic net, kernel ridge regression, support vector machines (SVR), partial least squares (PLS); classification - Linear Discriminant Analysis (LDA).
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