I was working on naive bayes for text classification using your package. I was using textmodel_NB , for which you fixed a issue for the wrong priors using docfreq. Now its coming correct but the predictions should also be changed Please see the below example as mentioned in your package:
library(quanteda)
trainingset <- as.dfm(matrix(c(1, 2, 0, 0, 0, 0,
0, 2, 0, 0, 1, 0,
0, 1, 0, 1, 0, 0,
0, 1, 1, 0, 0, 1,
0, 3, 1, 0, 0, 1),
ncol=6, nrow=5, byrow=TRUE,
dimnames = list(docs = paste("d", 1:5, sep = ""),
features = c("Beijing", "Chinese", "Japan", "Macao",
"Shanghai", "Tokyo"))))
trainingclass <- factor(c("Y", "Y", "Y", "N", NA), ordered = TRUE)
## replicate IIR p261 prediction for test set (document 5)
(nb.p261 <- textmodel_NB(trainingset, trainingclass,prior="docfreq"))
predict(nb.p261, newdata = trainingset[5, ])Output:
Fitted Naive Bayes model:
Call:
textmodel_NB(x = trainingset, y = trainingclass, prior = "docfreq")
Training classes and priors:
N Y
0.25 0.75
Likelihoods: Class Posteriors:
6 x 4 Matrix of class "dgeMatrix"
Y N Y N
Beijing 0.14285714 0.1111111 0.30000000 0.7000000
Chinese 0.42857143 0.2222222 0.39130435 0.6086957
Japan 0.07142857 0.2222222 0.09677419 0.9032258
Macao 0.14285714 0.1111111 0.30000000 0.7000000
Shanghai 0.14285714 0.1111111 0.30000000 0.7000000
Tokyo 0.07142857 0.2222222 0.09677419 0.9032258This is coming correct now
Predicted textmodel of type: Naive Bayes
lp(N) lp(Y) Pr(N) Pr(Y) Predicted
**d5 -9.206303 -7.808069 0.1981 0.8019 N**The prediction should be Y as Pr(Y)>Pr(N) but its is giving N
Please fix it to get the correct predictions.
I was working on naive bayes for text classification using your package. I was using textmodel_NB , for which you fixed a issue for the wrong priors using docfreq. Now its coming correct but the predictions should also be changed Please see the below example as mentioned in your package:
Output:
This is coming correct now
The prediction should be Y as Pr(Y)>Pr(N) but its is giving N
Please fix it to get the correct predictions.