text classification using logistic regression
If you have 3 classes and labelled data and have trained the model, then you have "told the classifier" everything you can (ie trained).
If you're saying you want to tell the classifier about the 2/6 test cases that failed, you then no its not possible with Logistic Regression (maybe some other feedback model?).  What you need is to train the model more, or add more test cases.  You could add those 2 failed cases to training and try different test data.
You may have an underfit model you can try to tune, but with the experiments I've done with text similar to yours it can be difficult to obtain really high accuracy with limited data and just tf-idf since the "model" is just word frequencies.
I am planning to classify emails. I am using tfidf vectorizer and logistic regression algorithm to do this. I took very small training and testing sets. My training set consists of 150 emails( 3 classes, 50 emails/class) and testing set consists of 6 emails. Now my classifier is predicting 4 out of 6 correctly. Now my doubt is, can I tell the classifier that this document belongs to class X not class Y? If yes, What is this process called?
Thank you.