Changeset 9904:a4f7bd7922d8 in orange
 Timestamp:
 02/07/12 11:29:21 (2 years ago)
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 default
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 db65f345b1af5b485bea743e63619c6a6d4753f5
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docs/reference/rst/Orange.evaluation.scoring.rst
r9892 r9904 114 114 data set, we would compute the matrix like this:: 115 115 116 cm = Orange.evaluation.scoring.confusion_matrices(resVeh, \ 117 vehicle.domain.classVar.values.index("van")) 116 cm = Orange.evaluation.scoring.confusion_matrices(resVeh, vehicle.domain.classVar.values.index("van")) 118 117 119 118 and get the results like these:: … … 177 176 classes, you can also compute the 178 177 `sensitivity <http://en.wikipedia.org/wiki/Sensitivity_(tests)>`_ 179 [TP/(TP+FN)], `specificity \ 180 <http://en.wikipedia.org/wiki/Specificity_%28tests%29>`_ 181 [TN/(TN+FP)], `positive predictive value \ 182 <http://en.wikipedia.org/wiki/Positive_predictive_value>`_ 183 [TP/(TP+FP)] and `negative predictive value \ 184 <http://en.wikipedia.org/wiki/Negative_predictive_value>`_ [TN/(TN+FN)]. 178 [TP/(TP+FN)], `specificity <http://en.wikipedia.org/wiki/Specificity_%28tests%29>`_ 179 [TN/(TN+FP)], `positive predictive value <http://en.wikipedia.org/wiki/Positive_predictive_value>`_ 180 [TP/(TP+FP)] and `negative predictive value <http://en.wikipedia.org/wiki/Negative_predictive_value>`_ [TN/(TN+FN)]. 185 181 In information retrieval, positive predictive value is called precision 186 182 (the ratio of the number of relevant records retrieved to the total number … … 195 191 as F1 [2*precision*recall/(precision+recall)] or, for a general case, 196 192 Falpha [(1+alpha)*precision*recall / (alpha*precision + recall)]. 197 The `Matthews correlation coefficient \ 198 <http://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_ 193 The `Matthews correlation coefficient <http://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_ 199 194 in essence a correlation coefficient between 200 195 the observed and predicted binary classifications; it returns a value
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