Logistic Regression for non-binary classification (multi-class) in R I am trying to use glm(family = binomial(link = 'logit')) for a classification task with 14 classes. I know that logistic regression is used in R for binary classification and as a result it outputs the probabilities for the predicted value being either 0 or 1. But is it possible to also use it for a non-binary classification task?
I have 14 classes and 93 features in my dataset.
This is how I have written it, and of course it does not work, because this is the approach that I use when I only have two classes;
log.model <- glm(fold1$class ~ . - id, data=fold1, family = binomial(link = 'logit'))
predict.glm(log.model, newdata=fold1.test.set, type = "response")

 A: As you note glm won't do it: the family=binomial part, implies two-way, not multi-way.
To look through packages you already have installed, try ??multinomial and look through the results. Among others the nnet package has a multinom, and there are several Bayesian R packages that support multinomial logistic regression including brms. (You can also do searches like ??"multinomial logistic" or ??"ordinal logistic".) For packages you don't have installed, search on CRAN.
You should also specify whether the categories have any ordering or not. If there is some ordering, such as "Small", "Medium", "Large", then you will probably want to use ordinal logistic regression rather than the more general categorical logistic regression.
A: Attempt a one-vs-all (aka one-vs-rest) system of logistic classifiers that proposes your problem as several binary classifiers.  That is train multiple binary classifiers--one for each of the 14 classes.  You will end up with 14 predictions.  The prediction that has the largest one-vs-all is the prediction--take the maximum probability (each classifier's prediction probability ) given by each classifier for each sample as the prediction.  
