Mastering Clojure Data Analysis by 2014

Author:2014
Language: eng
Format: mobi
Publisher: Packt Publishing

When executed, this function will return a list of ten of whatever the do-test function returns. In this case, that means a list of ten precision and recall mappings. We can average the output of this to get a summary of each classifier's performance.

Now we can start actually defining and testing classifiers.

Understanding maximum entropy classifiers

Maximum entropy (maxent) classifiers are, in a sense, very conservative classifiers. They assume nothing about hidden variables and base their classifications strictly upon the evidence they've been trained on. They are consistent with the facts that they've seen, but all other distributions are assumed to be completely uniform otherwise. What does this mean?

Let's say that we have a set of reviews and positive or negative ratings, and we wish to be able to predict the value of ratings when the ratings are unavailable, given the tokens or other features in the reviews. The probability that a rating is positive would be p(+). Initially, before we see any actual evidence, we may intuit that this probability would be uniform across all possible features. So, for a set of five features, before training, we might expect the probability function to return these values:

p(+)

½

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.