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Update learning_curve.rst (#22086)
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doc/modules/learning_curve.rst

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@@ -53,9 +53,9 @@ Validation curve
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To validate a model we need a scoring function (see :ref:`model_evaluation`),
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for example accuracy for classifiers. The proper way of choosing multiple
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hyperparameters of an estimator are of course grid search or similar methods
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hyperparameters of an estimator is of course grid search or similar methods
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(see :ref:`grid_search`) that select the hyperparameter with the maximum score
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on a validation set or multiple validation sets. Note that if we optimized
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on a validation set or multiple validation sets. Note that if we optimize
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the hyperparameters based on a validation score the validation score is biased
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and not a good estimate of the generalization any longer. To get a proper
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estimate of the generalization we have to compute the score on another test

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