I have a dataset that consists of 365 records, and I want to apply a classification model on it (binary classification).
As an output, in addition to the classification labels, I want to retrieve the classification confidence for each instance.
I don't know how to deal with such a case. Can I use, for example, linear classifiers (SVM, logistic regression) with this small dataset? Because, I want to retrieve the classification confidence as well.
I read that decision trees can be a good classifier for small datasets, but how can I retrieve the classification confidence with it?
The dataset consists of tweets, each classified as positive or negative (from a sentiment perspective), and my feature vector consists of 2400 features (combination between word2vec embeddings and other features).
Also, do you recommend me to use word2vec embeddings with such a small dataset? I think the classifier can't learn something from them using small dataset.