I would like to ask you how to use classifier and determine accuracy of models. I have my dataset and I already cleaned the text (remove stopwords, punctuation, removed empty rows,...). Then I split it into train and test. Since I want to determine if an email is spam or not, I have used the common classifiers, I.e. Naive Bayes, SVM and logistic regression. Here I just included my train and test datasets: nothing else! I am using Python to run this analysis. My question is: should it be enough or should I implement new algorithms?
If you could provide me with an example of how an already existing algorithm was improved it would be also good.
I read a lot of literature regarding accuracy of text classification and in all the papers the authors use SVM, Naïve Bayes, logistic regression to classify spam. But I do not know if they built their own classifier or just used the existing one in Python.
Any experience on this?