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Model evaluation: quantifying the quality of predictions
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- There are 3 different APIs for evaluating the quality of of a model's
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+ There are 3 different APIs for evaluating the quality of a model's
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predictions:
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* **Estimator score method **: Estimators have a ``score `` method providing a
@@ -1121,7 +1121,7 @@ predictions.
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BS = \frac {1 }{N} \sum _{t=1 }^{N}(f_t - o_t)^2
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where : :math: `N` is the total number of predictions, :math: `f_t` is the
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- predicted probablity of the actual outcome :math: `o_t`.
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+ predicted probability of the actual outcome :math: `o_t`.
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Here is a small example of usage of this function:::
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Original file line number Diff line number Diff line change @@ -251,14 +251,14 @@ def test_fowlkes_mallows_score_properties():
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score_original = fowlkes_mallows_score (labels_a , labels_b )
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assert_almost_equal (score_original , expected )
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- # symetric property
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- score_symetric = fowlkes_mallows_score (labels_b , labels_a )
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- assert_almost_equal (score_symetric , expected )
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+ # symmetric property
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+ score_symmetric = fowlkes_mallows_score (labels_b , labels_a )
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+ assert_almost_equal (score_symmetric , expected )
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# permutation property
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score_permuted = fowlkes_mallows_score ((labels_a + 1 ) % 3 , labels_b )
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assert_almost_equal (score_permuted , expected )
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- # symetric and permutation(both together)
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+ # symmetric and permutation(both together)
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score_both = fowlkes_mallows_score (labels_b , (labels_a + 2 ) % 3 )
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assert_almost_equal (score_both , expected )
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