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Explanation of results for non-ML folks (results on the default supabase repo shown on the homepage): Codeball's precision is 0.99. It simply means that 99% PR
by apugoneappu 4y ago
Explanation of results for non-ML folks (results on the default supabase repo shown on the homepage):
Codeball's precision is 0.99. It simply means that 99% PRs that were predicted approvable by Codeball were actually approved. In layman, if Codeball says that a PR is approvable, you can be 99% sure that it is.
But recall is 48%, meaning that only 48% of actually approved PRs were predicted to be approvable. So Codeball incorrectly flagged 52% of the approvable PRs to be un-approvable, just to be safe.
So Codeball is like a strict bartender who only serves you when they are absolutely sure you're old enough. You may still be overage but Codeball's not serving you.
- l33t2328 4y agoThat’s still super useful. I’m assuming most PR’s are approvable. If that’s the case then this should cut down on time spent doing reviews by a lot.
- Der_Einzige 4y agoA LOT of ML applications should be exactly like this. I want systems with low recall that "flag" things but ultra ultra high precision. Many times, we get exactly the opposite - which is far worse!
- jwilber 4y agoHere's a visual explainer on Precision vs Recall (in the context of ML algorithms): https://mlu-explain.github.io/precision-recall/ https://mlu-explain.github.io/precision-recall/
- Zababa 4y agoSo basically, very few false positive but lots of false negative is the tradeoff made by Codeball?