Building Machine Learning Systems with Python Second Edition by 2015

Author:2015
Language: eng
Format: epub
Publisher: Packt Publishing

The best estimator indeed improves the P/R AUC by nearly 3.3 percent to now 70.2, with the settings shown in the previous code.

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

== Pos vs. rest == 0.889 0.010 0.509 0.041 == Neg vs. rest == 0.886 0.007 0.615 0.035

Have a look at the following plots:

Indeed, the P/R curves look much better (note that the plots are from the medium of the fold classifiers, thus, slightly diverging AUC values). Nevertheless, we probably still wouldn't use those classifiers. Time for something completely different…

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