Building Machine Learning Systems with Python by 2013

Author:2013
Language: eng
Format: epub
Publisher: Packt Publishing

The best estimator indeed improves the P/R AUC by nearly 3.3 percent to 70.2 with the setting that was printed earlier.

The devastating results for positive tweets against the rest and negative tweets against the rest will improve if we configure the vectorizer and classifier with those parameters that we have just found out:

== Pos vs. rest == 0.883 0.005 0.520 0.028 == Neg vs. rest == 0.888 0.009 0.631 0.031

Indeed, the P/R curves look much better (note that the graphs are from the medium of the fold classifiers, thus have slightly diverging AUC values):

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