Results for Exercise Sheet 11 (Margin size of Naive Bayes)

Please add your row to the table below, following the examples already there. The number of outliers is the number of points on the "wrong" side of the hyperplane implicitly computed by Naive Bayes (see Exercise 11.1). The margin size is the minium distance of the points from one class to the hyperplan + the minimum distance of the points from the other class to the hyperplane, ignoring all outliers.

NOTE: the first line below the headers provides the results for an SVM classifier, as explained and demonstrated in the lecture. Note that the SVM classifier manages to completely linearly separate the training set in both cases, and still the margin size is larger. However, the precision increase only for one of the data sets, so it's not clear how much a larger margin size helps prediction accuracy.

Name	Actor vs. Politician			Singer vs. Songwriter
Name	#outliers	margin size	precision	#outliers	margin size	precision
SVM	0	0.66	98%	0	0.45	92%
Björn	5	0.01	98%	8	0.01	89%
MartinM	170	0.02	99%	818	0.001	96%
José	310	0.001	98%	1267	0.0004	88%
Freya & Wera	6	0.02	98%	10	0.001	57%
Anastasia	1185	0.913463	98%	442	0.478293	89%
MichaelR	308	0.000131	98%	855	0.000277	89%
Manuel	5	0.00002	98%	16	0.00001	89%
Stiglerj	286	0.0002	98%	738	0.0072	90%
Jens	5	0.01	98%	8	0.01	89%
Janosch	5	0.03	98%	803	inf	89%
Nico	621	---	98%	89	---	89%
Oier	319	2.4658823E-6	98%	897	4.883589E-7	89%
Melih & Gökçe	5	0.012	98%	7	0.009	89%