Project details for MLPY Machine Learning Py

Screenshot MLPY Machine Learning Py 2.0.4

by albanese - June 16, 2009, 13:56:57 CET [ Project Homepage BibTeX Download ]

view ( today), download ( today ), 2 comments, 0 subscriptions

OverallWhole StarWhole StarWhole Star1/2 StarEmpty Star
FeaturesWhole StarWhole StarWhole Star1/2 StarEmpty Star
UsabilityWhole StarWhole StarWhole Star1/2 StarEmpty Star
DocumentationWhole StarWhole StarWhole Star1/2 StarEmpty Star
(based on 3 votes)
Description:

Machine Learning PY (mlpy) is a high-performance Python package for predictive modeling. It makes extensive use of numpy (http://scipy.org) to provide fast N-dimensional array manipulation and easy integration of C code. mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. The package includes tools to measure stability in sets of ranked feature lists.

mlpy is a project of Predictive Models for Biological and Environmental Data Analysis (MPBA) Research Unit mpba.fbk.eu at Fondazione Bruno Kessler.

mlpy is cofinanced by Associazione Italiana per la Ricerca sul Cancro (AIRC).

Changes to previous version:
  • _imputing.py: purify() function added
  • imputing.py added; knnimputing() added
  • data_fromfile(): ytype parameter for label type added
  • knn.predict() fixed
BibTeX Entry: Download
Supported Operating Systems: Linux, Macosx, Windows, Unix, Freebsd
Data Formats: None
Tags: Svm, Classification, Clustering, Fda, Feature Weighting, Irelief, Rfe, Feature Ranking, Resampling, Srda, Nn, Dwt, Pda, Nips2008, Dlda, Wavelet, Imputing
Archive: download here

Comments

jacob Yang (on April 30, 2010, 14:24:11)
when the program is running, there is no output. I don't know when it will be finish.
Michele Filosi (on December 13, 2011, 10:04:04)
Very useful and well implemented!

Leave a comment

You must be logged in to post comments.