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
compatible with PyInstaller
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
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.mlpy