- 04/24/13 13:11:17 (12 months ago)
- 1 edited
r11422 r11477 73 73 optimization is to find those scatterplot projections, where instances with 74 74 different class labels are well separated. For example, for a data set 75 `brown-selected.tab <http://orange.biolab.si/doc/datasets/brown-selected.tab>`_ 75 brown-selected.tab 76 76 (comes with Orange installation) the two attributes that best separate 77 77 instances of different class are displayed in the snapshot below, where we have … … 148 148 outliers. The idea is that the outliers are those data instances, which are 149 149 incorrectly classified in many of the top visualizations. For example, the 150 class of the 33-rd instance in `brown-selected.tab 151 <http://orange.biolab.si/doc/datasets/brown-selected.tab>`_ should be Resp, 150 class of the 33-rd instance in brown-selected.tab should be Resp, 152 151 but this instance is quite often misclassified as Ribo. The snapshot below 153 152 shows one particular visualization displaying why such misclassification
Note: See TracChangeset for help on using the changeset viewer.