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1 | ################ |
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2 | Mean (``mean``) |
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3 | ################ |
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4 | |
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5 | .. py:currentmodule:: Orange.regression.mean |
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6 | |
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7 | .. index:: regression; mean |
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8 | |
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9 | Accuracy of a regressor is often compared with the accuracy achieved |
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10 | by always predicting the average value. The "learning algorithm" |
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11 | computes the average and represents it with a regressor of type |
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12 | :obj:`Orange.classification.ConstantClassifier`. |
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13 | |
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14 | .. rubric:: Examples |
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15 | |
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16 | The following example compares the mean squared error of always |
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17 | predicting the average with the error of a tree learner. |
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18 | |
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19 | :download:`mean-regression.py <code/mean-regression.py>`: |
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20 | |
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21 | .. literalinclude:: code/mean-regression.py |
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22 | :lines: 7- |
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