orange.LinearLearner
is a learner that uses the LIBLINEAR library backend that is very fast on large datasets.
Linear learner learnes the attribute weights using one of the four possible methods.
Attributes
orange.LinearLearner.L2_LR (L2-regularized logistic regression, default)
orange.LinearLearner.L2LOSS_SVM_DUAL
orange.LinearLearner.L2LOSS_SVM
orange.LinearLearner.L1LOSS_SVM_DUAL
L2_LR
supports probabilty esstimations.Linear classifiers that uses one class vs. rest strategy for multi-class classification. It supports probability esstimation only if it was build with L2-regularized logistic regression learner.
Attributes
Part of linear-learner.py
Produces the output:
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear