# how to interpret the coefficients of binary logistic regression?

I'm working on a kNN classifier to classify whether a text is written by a man or a woman on the basis of the most frequent words. However, a kNN doesn't show which features were the most important in the classification. I got the suggestion to fit a logistic regression on my training data and labels to see which words were the most important in the classification task.

Now I have a list of the top 20 positive words and coefficients and top 20 words with negative coefficients. I do not know how to interpret these. I can see that the logistic regression has the classes F and M (don't know which one is 0 and which is 1), but is it possible to argue that the positive coefficients belong to the F class and the negative belong to the M class?

• Write the equation for the classifier and see for yourself. May 29, 2017 at 10:31
• Do you remove the stop words? May 29, 2017 at 11:51
• What research have you done? How to compute feature importance / feature rankings for logistic regression is well documented in lots of places (including in multiple places on Cross Validated).
– D.W.
May 30, 2017 at 1:57

There is a section midway through on interpreting your regression coefficients. In your specific case (as I imagine all of your independent variables only take on positive values), your interpretation is fine. If $b_1$ is the slope coefficient of an indicator variable for some word, then a 1 unit change in $b_1$ (i.e. if the word corresponding to $b_1$ is seen) corresponds to an $\exp{(b_{1})}$ change in the log odds of your model (e.g. the probability of the person being a boy is $\exp{(b_{1})}$ more likely). Hope this helps