Building Machine Learning Systems with Python - Richert, Coelho

Classification – Detecting Poor Answers
Slimming the classifier
It is always worth looking at the actual contributions of the individual features.
For logistic regression, we can directly take the learned coefficients (clf.coef_)
to get an impression of the feature's impact. The higher the coefficient of a feature
is, the more the feature plays a role in determining whether the post is good
or not. Consequently, negative coefficients tell us that the higher values for the
corresponding features indicate a stronger signal for the post to be classified as bad:
We see that LinkCount and NumExclams have the biggest impact on the overall
classification decision, while NumImages and AvgSentLen play a rather minor role.
While the feature importance overall makes sense intuitively, it is surprising that
NumImages is basically ignored. Normally, answers containing images are always
rated high. In reality, however, answers very rarely have images. So although in
principal it is a very powerful feature, it is too sparse to be of any value. We could
easily drop this feature and retain the same classification performance.
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