![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8318400%2F74f6fc6f6923665dff86c45f8dfb5b36%2Ffbeta.png?generation=1731091510099439&alt=media\")
In this competition, the F-beta score with a high beta value of 4 is used to evaluate particle identification, which puts significantly more emphasis on recall than on precision. But what does this mean, and why does it matter?
\nThe F-beta score is a metric that combines precision (how many of your predicted particles are correct) and recall (how many actual particles you managed to detect). With beta > 1, recall is prioritized, meaning the scoring is more forgiving of false positives but heavily penalizes missed particles. Here’s the formula:
\n
If you see this formula in another form, it will look like this:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8318400%2F42972714e5d27168c07cbe770ef9c3b1%2Ffbeta2.png?generation=1731091720049022&alt=media\")
\nIf you see the recall term in denominator carefully, when a high beta value is kept, this recall term has already very high value which makes the overall F-Beta score very low, so value of recall has to be very high to compensate for this increased value of beta, which makes the overall F-beta value to increase. Thus we can see, increasing beta, penalizes the recall value very much, forcing recall to be very high.
But Why it’s relevant here:
\nIn this competition, the F-beta score with a high beta value of 4 is used to evaluate particle identification, which puts significantly more emphasis on recall than on precision. But what does this mean, and why does it matter?
\nThe F-beta score is a metric that combines precision (how many of your predicted particles are correct) and recall (how many actual particles you managed to detect). With beta > 1, recall is prioritized, meaning the scoring is more forgiving of false positives but heavily penalizes missed particles. Here’s the formula:
\n
If you see this formula in another form, it will look like this:
\n
\nIf you see the recall term in denominator carefully, when a high beta value is kept, this recall term has already very high value which makes the overall F-Beta score very low, so value of recall has to be very high to compensate for this increased value of beta, which makes the overall F-beta value to increase. Thus we can see, increasing beta, penalizes the recall value very much, forcing recall to be very high.
But Why it’s relevant here:
\nIf you look at the data it appears, at least for ribosomes, that many are unlabeled. That may also be part of the reason.
", "rawMarkdown": "If you look at the data it appears, at least for ribosomes, that many are unlabeled. That may also be part of the reason.", "votes": null }, { "id": "3040692", "postDate": "11/09/2024 13:14:39", "content": "Yeah, for that we have to include more examples either using generated data or by some technique of labelling ? What do u say ?
", "rawMarkdown": "Yeah, for that we have to include more examples either using generated data or by some technique of labelling ? What do u say ?", "votes": null }, { "id": "3041124", "postDate": "11/10/2024 01:09:37", "content": "Maybe, but with the exception of the viruses most of the structures are essentially just dots. May not need a whole lot of training data.
", "rawMarkdown": "Maybe, but with the exception of the viruses most of the structures are essentially just dots. May not need a whole lot of training data.", "votes": null }, { "id": "3041271", "postDate": "11/10/2024 05:18:54", "content": "Yeah. that makes sense
", "rawMarkdown": "Yeah. that makes sense", "votes": null } ], "comments": [ { "id": 3040272, "author_name": "davidlist", "author_url": "", "post_date": "11/08/2024 23:10:15", "content": "If you look at the data it appears, at least for ribosomes, that many are unlabeled. That may also be part of the reason.
", "votes": null, "replies": [ { "id": 3040692, "author_name": "arunimbasak", "author_url": "", "post_date": "11/09/2024 13:14:39", "content": "Yeah, for that we have to include more examples either using generated data or by some technique of labelling ? What do u say ?
", "votes": null, "replies": [ { "id": 3041124, "author_name": "davidlist", "author_url": "", "post_date": "11/10/2024 01:09:37", "content": "Maybe, but with the exception of the viruses most of the structures are essentially just dots. May not need a whole lot of training data.
", "votes": null, "replies": [ { "id": 3041271, "author_name": "arunimbasak", "author_url": "", "post_date": "11/10/2024 05:18:54", "content": "Yeah. that makes sense
", "votes": null, "replies": [] } ] } ] } ] } ], "raw_markdown_by_id": { "3040144": "In this competition, the F-beta score with a high beta value of 4 is used to evaluate particle identification, which puts significantly more emphasis on recall than on precision. But what does this mean, and why does it matter?\n\nThe F-beta score is a metric that combines precision (how many of your predicted particles are correct) and recall (how many actual particles you managed to detect). With beta > 1, recall is prioritized, meaning the scoring is more forgiving of false positives but heavily penalizes missed particles. Here’s the formula:\n\n\n\nIf you see this formula in another form, it will look like this:\n\nIf you see the recall term in denominator carefully, when a high beta value is kept, this recall term has already very high value which makes the overall F-Beta score very low, so value of recall has to be very high to compensate for this increased value of beta, which makes the overall F-beta value to increase. Thus we can see, increasing beta, penalizes the recall value very much, forcing recall to be very high.\n\nBut Why it’s relevant here: \n1. Emphasizes Finding All Particles: Since missing particles is more critical than false alarms, a higher recall is crucial for scientific insights.\n2. Handles Hard Particles: This beta setting encourages models to detect both \"easy\" and \"hard\" particles, where the latter may be challenging but essential to capture.", "3040272": "If you look at the data it appears, at least for ribosomes, that many are unlabeled. That may also be part of the reason.", "3040692": "Yeah, for that we have to include more examples either using generated data or by some technique of labelling ? What do u say ?", "3041124": "Maybe, but with the exception of the viruses most of the structures are essentially just dots. May not need a whole lot of training data.", "3041271": "Yeah. that makes sense" }, "source": "meta" }