Changeset 11477:c1fdcc4deb7b in orange
- 04/24/13 13:11:17 (11 months ago)
- 3 edited
r10393 r11477 71 71 .. autoclass:: DiscretizeTable(features=None, discretize_class=False, method=EqualFreq(n=3), clean=True) 72 72 73 .. A chapter on `feature subset selection <../ofb/o_fss.htm>`_in Orange 73 .. A chapter on in Orange 74 74 for Beginners tutorial shows the use of DiscretizedLearner. Other 75 75 discretization classes from core Orange are listed in chapter on 76 `categorization <../ofb/o_categorization.htm>`_of the same tutorial. -> should put in classification/wrappers 76 of the same tutorial. -> should put in classification/wrappers 77 77 78 78 .. [FayyadIrani1993] UM Fayyad and KB Irani. Multi-interval discretization of continuous valued
r11359 r11477 52 52 See the documentation on :ref:`Radviz` for details on various aspects 53 53 controlled by the :obj:`Settings` tab. The utility of VizRank, an intelligent 54 visualization technique, using `brown-selected.tab 55 <http://orange.biolab.si/doc/datasets/brown-selected.tab>`_ data set is 54 visualization technique, using brown-selected.tab data set is 56 55 illustrated with a snapshot below. 57 56
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
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