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mlpy
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mlpy

Overview
Synopsis

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.

Category

Data Mining Software Free

Features

•Python method for machine learning
•Provides high-level functions and classes
•Works with Python 2 and 3
•Open Source
•Compatible with PyInstaller

License

Open Source

Price

Free

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

mlpy

What is best ?

•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

Rating
Our Rating
User Rating
Ease of use
7.6
8.3
Features & Functionality
7.6
8.2
Advanced Features
7.6
8.3
Integration
7.6
8.3
Customer Support
7.6
Performance
7.6
Training
Implementation
Renew & Recommend
Bottom Line

mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3.

7.6
Our Rating
8.3
User Rating
1 rating
You have rated this

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), Basic Perceptron, Elastic Net, Logistic Regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based etc; clustering - Hierarchical Clustering, Memory-saving Hierarchical Clustering, k-means; dimensionality reduction - (Kernel) Fisher Discriminant (FDA), Spectral Regression Discriminant Analysis (SRDA), (kernel) Principal Component Analysis (PCA); Wavelet Submodule - Discrete, Undecimated and Continuous Wavelet Transform; Misc - Feature ranking/selection algorithms, Canberra stability indicator, resampling algorithms, error evaluation, peak finding algorithms etc. Mpyl platform is completely compatible with PyInstaller.

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1 Reviews
  • Ernestina Thorn
    September 11, 2017 at 11:33 am

    Advanced machine learning methods of supervised and unsupervised problems

    Company size

    Enterprise (>1001)

    User Role

    IT Support

    User Industry

    Financial services

    Rating
    Ease of use8.3

    Features & Functionality8.2

    Advanced Features8.3

    Integration8.3

    ADDITIONAL INFORMATION
    mlpy is a Python module for Machine learning built on top of NumPy/SciPy and the GNU scientific libraries. It is open source software distributed under the GNU General Public License. mlpy provides a variety of advanced machine learning methods of supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability, and efficiency. mlpy allows users to perform linear regression analysis including least squares, partial least squares, ridge regression, last angle regression, elastic net, kernel ridge regression, and support vector machines (SVR). You can also perform linear classification which entails the following features; Linear Discriminant Analysis (LDA), Basic Perceptron, Elastic Net, Logistic Regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier k-Nearest-Neighbor, Iterative RELIEF, Classification Tree, and Maximum Likelihood Classifier. mlpy lets users manipulate data and perform clustering operations on it using these methods; hierarchical clustering, memory-saving hierarchical clustering, and k-means method. mlpy provides online documentation and reference manual that detail the functions, modules, and objects included in mlpy.

Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Training
Implementation
Renew & Recommend

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