Naive Bayes and Logistic Regression Error Rate
I have been trying to figure out the correlation between the error rate
and the number of features in both of these models. I watched some videos,
and the creator of the video said that a simple model can be better than a
complicated model. So I figured that the more features I had the greater
the error rate would be. This did not prove to be true in my work, and
when I had less features the error rate went up. I'm not sure if I'm doing
this incorrectly, or if the guy in the video made a mistake. Can someone
care to explain? I also am curious how features relate to Logistic
Regression's error rate as well.
No comments:
Post a Comment