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• Cross validation for model evaluation • Automatic parameter selection • Probability estimates (logistic regression only) • Weights for unbalanced data • MATLAB/Octave, Java, Python, Ruby interfaces
What are the benefits?
• Speedups the training on shared memory systems • Supports L2-regularized classifiers • Supports L2-loss linear SVR and L1-loss linear SVR • Supports L2-loss linear SVM and logistic regression • Supports L1-regularized classifiers
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Ease of use
7.6
8.8
Features & Functionality
7.6
9.5
Advanced Features
7.6
9.2
Integration
7.6
9.2
Performance
7.6
8.9
Customer Support
7.6
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Bottom Line
LIBLINEAR is an open source library that comes with easy to use command tools and library calls that enable developers, end users, and data scientists perform large scale linear classification.
7.6
Editor Rating
9.1
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LIBLINEAR is an open source library that is used by data scientists, developers and end users to perform large scale linear classification. The easy to use command tools and library calls enables LIBLINEAR to be used by data scientists and developers to perform logistics, regression and linear support for vector machine.
With LIBLINEAR developers and data scientists are able to same data format as the one in LIBSVM found in LINLINEAR general purpose SVM solver which also has similar usage. LINLINEAR presents several machine language interfaces that can be used by data scientists and developers. The machine language interfaces presented are Python, Java, MATLAB and Ruby interfaces. Data scientists and developers dealing with unbalanced data in LIBLINEAR are also sorted out, as they are provided with weights to use when handling this.
LIBLINEAR also enables developers and end users perform model evaluation and cross validation using their preferred machine language. Developers and end users performing linear regression only in LIBLINEAR are provided by the probability estimates which therefore make linear regression calculations easy.
LIBLINEAR also provides the multi-class classification feature. The multi-class classification feature is classified into two. The first classification is one-vs-the res and the second classification is Crammer and Singer.
The multi-classification feature enables developers and data scientists use different classes when performing large scale linear classification. LIBLINEAR also features the automatic parameter selection that provides parameters used in large scale linear classification by data scientists and developers. LIBLINEAR also allows developers and data scientists to use large data which in some cases do not have nonlinear mappings but still provides similar performances.
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