.. py:currentmodule:: Orange.evaluation.scoring ############################# Scoring methods (``scoring``) ############################# CA -- .. index:: CA .. autofunction:: Orange.evaluation.CA Precision --------- .. index:: Precision .. autofunction:: Orange.evaluation.Precision Recall ------ .. index:: Recall .. autofunction:: Orange.evaluation.Recall F1 -- .. index:: F1 .. autofunction:: Orange.evaluation.F1 PrecisionRecallFSupport ----------------------- .. index:: PrecisionRecallFSupport .. autofunction:: Orange.evaluation.PrecisionRecallFSupport AUC -------- .. index:: AUC .. autofunction:: Orange.evaluation.AUC Log Loss -------- .. index:: Log loss .. autofunction:: Orange.evaluation.LogLoss MSE --- .. index:: MSE .. autofunction:: Orange.evaluation.MSE MAE --- .. index:: MAE .. autofunction:: Orange.evaluation.MAE R2 -- .. index:: R2 .. autofunction:: Orange.evaluation.R2 CD diagram ---------- .. index:: CD diagram .. autofunction:: Orange.evaluation.compute_CD .. autofunction:: Orange.evaluation.graph_ranks Example ======= >>> import Orange >>> import matplotlib.pyplot as plt >>> names = ["first", "third", "second", "fourth" ] >>> avranks = [1.9, 3.2, 2.8, 3.3 ] >>> cd = Orange.evaluation.compute_CD(avranks, 30) #tested on 30 datasets >>> Orange.evaluation.graph_ranks(avranks, names, cd=cd, width=6, textspace=1.5) >>> plt.show() The code produces the following graph: .. image:: images/statExamples-graph_ranks1.png