View source: R/textmodel_svm.R
textmodel_svm | R Documentation |
Fit a fast linear SVM classifier for texts, using the LiblineaR package.
textmodel_svm( x, y, weight = c("uniform", "docfreq", "termfreq"), type = 1, ... )
x |
the dfm on which the model will be fit. Does not need to contain only the training documents. |
y |
vector of training labels associated with each document identified
in |
weight |
weights for different classes for imbalanced training sets,
passed to |
type |
argument passed to the |
... |
additional arguments passed to |
R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. (2008) LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9: 1871-1874. https://www.csie.ntu.edu.tw/~cjlin/liblinear/.
LiblineaR::LiblineaR()
predict.textmodel_svm()
# use party leaders for govt and opposition classes library("quanteda") docvars(data_corpus_irishbudget2010, "govtopp") <- c(rep(NA, 4), "Gov", "Opp", NA, "Opp", NA, NA, NA, NA, NA, NA) dfmat <- dfm(tokens(data_corpus_irishbudget2010)) tmod <- textmodel_svm(dfmat, y = dfmat$govtopp) predict(tmod) # multiclass problem - all party leaders tmod2 <- textmodel_svm(dfmat, y = c(rep(NA, 3), "SF", "FF", "FG", NA, "LAB", NA, NA, "Green", rep(NA, 3))) predict(tmod2)
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