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Doc fix early stopping #19818
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Doc fix early stopping #19818
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Thank you for the PR @isaack-mungui !
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Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
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I slightly prefer my suggestion from #19818 (comment) but I am also fine with the current state of the PR.
Thanks for the PR @isaack-mungui , I agree with @ogrisel that his suggestion is more clear to users:
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* Maintenance task: Moved PolynomialFeatures to _polynomial.py * Updated docstring including behaviour of neural network when early stopping is activated * Revert "Maintenance task: Moved PolynomialFeatures to _polynomial.py" This reverts commit f76df54. * reverted failing commit * Update sklearn/neural_network/_multilayer_perceptron.py Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * Updated doc with proposed changes * Fixed whitespace error Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* Maintenance task: Moved PolynomialFeatures to _polynomial.py * Updated docstring including behaviour of neural network when early stopping is activated * Revert "Maintenance task: Moved PolynomialFeatures to _polynomial.py" This reverts commit f76df54. * reverted failing commit * Update sklearn/neural_network/_multilayer_perceptron.py Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * Updated doc with proposed changes * Fixed whitespace error Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* Maintenance task: Moved PolynomialFeatures to _polynomial.py * Updated docstring including behaviour of neural network when early stopping is activated * Revert "Maintenance task: Moved PolynomialFeatures to _polynomial.py" This reverts commit f76df54. * reverted failing commit * Update sklearn/neural_network/_multilayer_perceptron.py Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * Updated doc with proposed changes * Fixed whitespace error Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Reference Issues/PRs
Towards #19743
What does this implement/fix? Explain your changes.
Highlights the behaviour of a neural network if early_stopping is activated: in this case, validation score is used to stop the neural network. Otherwise, loss is used.