8000 Fix typos (#9265) · CoderPat/scikit-learn@5d7df62 · GitHub
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taehoonleeTomDLT
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Fix typos (scikit-learn#9265)
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doc/modules/model_evaluation.rst

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Model evaluation: quantifying the quality of predictions
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========================================================
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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
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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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sklearn/metrics/cluster/tests/test_supervised.py

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@@ -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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