For me personally, I tried 5 folds and single fold. Single fold LB improved 0.02. What about yours?
", "messages": [ { "id": "1781061", "postDate": "05/08/2022 07:01:41", "content": "For me personally, I tried 5 folds and single fold. Single fold LB improved 0.02. What about yours?
", "rawMarkdown": "For me personally, I tried 5 folds and single fold. Single fold LB improved 0.02. What about yours?", "votes": null }, { "id": "1781259", "postDate": "05/08/2022 11:06:27", "content": "Do you mean your single fold model performs better than 5 fold ensemble? That's unusual xD.
", "rawMarkdown": "Do you mean your single fold model performs better than 5 fold ensemble? That's unusual xD.", "votes": null }, { "id": "1781271", "postDate": "05/08/2022 11:21:11", "content": "5-fold or rather k-fold usually performs better than a single fold. Kindly recheck the model parameters.
", "rawMarkdown": "5-fold or rather k-fold usually performs better than a single fold. Kindly recheck the model parameters.", "votes": null }, { "id": "1781307", "postDate": "05/08/2022 11:56:47", "content": "Maybe there is a problem with my ensemble method.
", "rawMarkdown": "Maybe there is a problem with my ensemble method.", "votes": null }, { "id": "1781308", "postDate": "05/08/2022 11:57:26", "content": "I will check them. Thanks for your answer!
", "rawMarkdown": "I will check them. Thanks for your answer!", "votes": null }, { "id": "1781372", "postDate": "05/08/2022 12:53:54", "content": "What about CV? The problem with single fold is it tends to be more sensitive to random fluctuations. So you could just have gotten lucky on the public LB (or not). Just to point out - people have had success combining both 5 folds and single fold (in other competitions), maybe something to try.
", "rawMarkdown": "What about CV? The problem with single fold is it tends to be more sensitive to random fluctuations. So you could just have gotten lucky on the public LB (or not). Just to point out - people have had success combining both 5 folds and single fold (in other competitions), maybe something to try.", "votes": null }, { "id": "1781477", "postDate": "05/08/2022 14:46:40", "content": "I have 0.1 LB fluctuations across folds at the moment, and just like you my single fold is better than 5 fold :(
", "rawMarkdown": "I have 0.1 LB fluctuations across folds at the moment, and just like you my single fold is better than 5 fold :(", "votes": null }, { "id": "1781764", "postDate": "05/08/2022 23:29:40", "content": "The purpose of cross validation is to mitigate the effects of statistical bias in the training data; a good result to one fold does not necessarily reflect the true performance of the model.
", "rawMarkdown": "The purpose of cross validation is to mitigate the effects of statistical bias in the training data; a good result to one fold does not necessarily reflect the true performance of the model.", "votes": null }, { "id": "1782024", "postDate": "05/09/2022 06:58:40", "content": "Not really unusual. No problem with ensemble. This behaviour is expected (in this comp). Of course once we expect this behaviour we can next think of how to deal with it
", "rawMarkdown": "Not really unusual. No problem with ensemble. This behaviour is expected (in this comp). Of course once we expect this behaviour we can next think of how to deal with it", "votes": null }, { "id": "1782820", "postDate": "05/09/2022 21:56:31", "content": "5-fold cross validation is a very reliable way to train a model. It helps prevent overfitting and usually leads to a better model.
", "rawMarkdown": "5-fold cross validation is a very reliable way to train a model. It helps prevent overfitting and usually leads to a better model.", "votes": null } ], "comments": [ { "id": 1781259, "author_name": "leonshangguan", "author_url": "", "post_date": "05/08/2022 11:06:27", "content": "Do you mean your single fold model performs better than 5 fold ensemble? That's unusual xD.
", "votes": null, "replies": [ { "id": 1781307, "author_name": "yangranran", "author_url": "", "post_date": "05/08/2022 11:56:47", "content": "Maybe there is a problem with my ensemble method.
", "votes": null, "replies": [] }, { "id": 1782024, "author_name": "allohvk", "author_url": "", "post_date": "05/09/2022 06:58:40", "content": "Not really unusual. No problem with ensemble. This behaviour is expected (in this comp). Of course once we expect this behaviour we can next think of how to deal with it
", "votes": null, "replies": [] } ] }, { "id": 1781271, "author_name": "ravi20076", "author_url": "", "post_date": "05/08/2022 11:21:11", "content": "5-fold or rather k-fold usually performs better than a single fold. Kindly recheck the model parameters.
", "votes": null, "replies": [ { "id": 1781308, "author_name": "yangranran", "author_url": "", "post_date": "05/08/2022 11:57:26", "content": "I will check them. Thanks for your answer!
", "votes": null, "replies": [] } ] }, { "id": 1781372, "author_name": "motloch", "author_url": "", "post_date": "05/08/2022 12:53:54", "content": "What about CV? The problem with single fold is it tends to be more sensitive to random fluctuations. So you could just have gotten lucky on the public LB (or not). Just to point out - people have had success combining both 5 folds and single fold (in other competitions), maybe something to try.
", "votes": null, "replies": [] }, { "id": 1781477, "author_name": "nyleve", "author_url": "", "post_date": "05/08/2022 14:46:40", "content": "I have 0.1 LB fluctuations across folds at the moment, and just like you my single fold is better than 5 fold :(
", "votes": null, "replies": [] }, { "id": 1781764, "author_name": "tatamikenn", "author_url": "", "post_date": "05/08/2022 23:29:40", "content": "The purpose of cross validation is to mitigate the effects of statistical bias in the training data; a good result to one fold does not necessarily reflect the true performance of the model.
", "votes": null, "replies": [] }, { "id": 1782820, "author_name": "", "author_url": "", "post_date": "05/09/2022 21:56:31", "content": "5-fold cross validation is a very reliable way to train a model. It helps prevent overfitting and usually leads to a better model.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1781061": "For me personally, I tried 5 folds and single fold. Single fold LB improved 0.02. What about yours?", "1781259": "Do you mean your single fold model performs better than 5 fold ensemble? That's unusual xD.", "1781271": "5-fold or rather k-fold usually performs better than a single fold. Kindly recheck the model parameters.", "1781307": "Maybe there is a problem with my ensemble method.", "1781308": "I will check them. Thanks for your answer!", "1781372": "What about CV? The problem with single fold is it tends to be more sensitive to random fluctuations. So you could just have gotten lucky on the public LB (or not). Just to point out - people have had success combining both 5 folds and single fold (in other competitions), maybe something to try.", "1781477": "I have 0.1 LB fluctuations across folds at the moment, and just like you my single fold is better than 5 fold :(", "1781764": "The purpose of cross validation is to mitigate the effects of statistical bias in the training data; a good result to one fold does not necessarily reflect the true performance of the model.", "1782024": "Not really unusual. No problem with ensemble. This behaviour is expected (in this comp). Of course once we expect this behaviour we can next think of how to deal with it", "1782820": "5-fold cross validation is a very reliable way to train a model. It helps prevent overfitting and usually leads to a better model." }, "source": "meta" }