机器学习各类工具weka、scikit-learn等各项指标的对比

以下表格摘自：http://www.shogun-toolbox.org/

另推荐机器学习软件汇总网站 http://mloss.org/software/

feature	shogun	weka	kernlab	dlib	nieme	orange	java-ml	pyML	mlpy	pybrain	torch3	scikit-learn
General Features	Graphical User Interface
	One Class Classification
	Classification
	Multiclass classification
	Regression
	Structured Output Learning
	Pre-Processing
	Built-in Model Selection Strategies
	Visualization
	Test Framework
	Large Scale Learning
	Semi-supervised Learning
	Multitask Learning
	Domain Adaptation
	Serialization
	Parallelized Code
	Performance Measures (auROC etc)
	Image Processing
Supported Operating Systems	Linux
	Windows
	Mac OSX
	Other Unix
Language Bindings	Python
	R
	Matlab
	Octave
	C/C++
	Command Line
	Java
	C#
	Lua
	Ruby
SVM Solvers	SVMLight
	LibSVM
	SVM Ocas
	LibLinear
	BMRM
	LaRank
	SVMPegasos
	SVM SGD
	other
Regression	Kernel Ridge Regression
	Support Vector Regression
	Gaussian Processes
	Relevance Vector Machine
Multiple Kernel Learning	MKL
	q-norm MKL
Classifiers	Naive Bayes
	Bayesian Networks
	Multi Layer Perceptron
	RBF Networks
	Logistic Regression
	LASSO
	Decision Trees
	k-NN
Linear Classifiers	Linear Programming Machine
	LDA
Distributions	Markov Chains
	Hidden Markov Models
Kernels	Linear
	Gaussian
	Polynomial
	String Kernels
	Sigmoid Kernel
	Kernel Normalizer
Feature Selection	Forward
	Wrapper methods
	Recursive Feature Selection
Missing Features	Mean value imputation
	EM-based/model based imputation
Clustering	Hierarchical Clustering
	k-means
Optimization	BFGS
	conjugate gradient
	gradient descent
	bindings to CPLEX
	bindings to Mosek
	bindings to other solver
Supported File Formats	Binary
	Arff
	HDF5
	CSV
	libSVM/ SVMLight format
	Excel
Supported Data Types	Sparse Data Representation
	Dense Matrices
	Strings
	Support for native (e.g. C) types (char, signed and unsigned int8, int16, int32, int64, float, double, long double)