Added how-to guide: k-fold cross-validation #43
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sdesrozis
reviewed
Oct 1, 2021
sdesrozis
reviewed
Oct 1, 2021
| "acc_sum = 0\n", | ||
| "for n_fold in range(len(results_per_fold)):\n", | ||
| " current_fold = results_per_fold[n_fold]\n", | ||
| " print(f\"Validation Results - Epoch[{n_fold + 1}] Avg accuracy: {current_fold[1][2]['Accuracy']:.2f} Avg loss: {current_fold[1][2]['Loss']:.2f}\")\n", |
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current_fold[1][2]["Accuracy"] means getting the Accuracy score of the validation ([1]) at epoch 3 ([2]). Maybe it worth mention that.
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@LiviaCavalcanti Thank you for this great contribution ! I left a few comments. |
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@LiviaCavalcanti thanks a lot for the PR ! Looks good to me. Please fix the comments by @sdesrozis and it is good to go. |
vfdev-5
approved these changes
Oct 7, 2021
Priyansi
suggested changes
Oct 7, 2021
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@LiviaCavalcanti thank you for this amazing PR! I've left some comments and this PR should be good to merge as soon as they are addressed. |
Co-authored-by: Priyansi <[email protected]> Co-authored-by: Sylvain Desroziers <[email protected]>
Priyansi
reviewed
Oct 12, 2021
Priyansi
reviewed
Oct 12, 2021
Priyansi
reviewed
Oct 12, 2021
Priyansi
reviewed
Oct 12, 2021
Priyansi
approved these changes
Oct 12, 2021
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Resolves #28.
A how-to guide of training model with cross-validation using the k-fold technique to estimate its performance. For data split into the folds, the scikit-learn library was used.