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I am trying to use Naive Bayes to perform text classification. I have two classes A and B. I am mainly interested in identifying class A.

Description about the dataset:

  1. Some of the text contents that belong to class A can also be found in class B.
  2. But some of the text contents are only found in class A.
  3. The entire dataset is pretty small.

Training Set:

Number of Datapoints for class A: 275

Number of Datapoints for class B: 691

Testing Set:

Number of Datapoints for class A: 238

Number of Datapoints for class B: 403

I am using sci-kit learn's multinomialNB to perform the text classification.

The result that I got after running the Naive Bayes classifier on my test set:

Number of datapoints for class A (True Positive): 0

Number of datapoints for class B (True Negative): 401

Number of datapoints for class A (False Positive): 2

Number of datapoints for class B (False Negative): 238

EDIT:

I would like to know how can I improve the classification result of the aforementioned dataset?

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  • $\begingroup$ What actualy is your question...? $\endgroup$
    – NeuroMorphing
    Commented Apr 25, 2017 at 23:25
  • $\begingroup$ @Unhandledexception edited my question? $\endgroup$
    – John Rambo
    Commented Apr 26, 2017 at 0:18

1 Answer 1

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You can try using a different classifier like logistic regression. Logistic regression is easier to regularize and that will help you avoid the problem of always predicting the same class.

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