[{"data":1,"prerenderedAt":72},["ShallowReactive",2],{"term-p\u002Fprecision":3,"related-p\u002Fprecision":58},{"id":4,"title":5,"acronym":6,"body":7,"category":40,"description":41,"difficulty":42,"extension":43,"letter":16,"meta":44,"navigation":45,"path":46,"related":47,"seo":52,"sitemap":53,"stem":56,"subcategory":6,"__hash__":57},"terms\u002Fterms\u002Fp\u002Fprecision.md","Precision",null,{"type":8,"value":9,"toc":33},"minimark",[10,15,19,23,26,30],[11,12,14],"h2",{"id":13},"eli5-the-vibe-check","ELI5 — The Vibe Check",[16,17,18],"p",{},"Precision asks: 'Of all the times the AI said YES, how often was it actually right?' High precision means when the model flags something, it's almost always correct. A spam filter with high precision rarely marks real emails as spam. The tradeoff: being very careful means you might miss some actual spam.",[11,20,22],{"id":21},"real-talk","Real Talk",[16,24,25],{},"Precision is TP \u002F (TP + FP) — the fraction of positive predictions that are actually positive. High precision indicates low false positive rate. Precision is prioritized when false positives are costly (e.g., fraud alerts, medical diagnoses). It trades off against recall; improving one typically degrades the other.",[11,27,29],{"id":28},"when-youll-hear-this","When You'll Hear This",[16,31,32],{},"\"High precision means fewer false positives.\" \u002F \"Precision matters more here than recall.\"",{"title":34,"searchDepth":35,"depth":35,"links":36},"",2,[37,38,39],{"id":13,"depth":35,"text":14},{"id":21,"depth":35,"text":22},{"id":28,"depth":35,"text":29},"ai","Precision asks: 'Of all the times the AI said YES, how often was it actually right?","intermediate","md",{},true,"\u002Fterms\u002Fp\u002Fprecision",[48,49,50,51],"Recall","F1 Score","Accuracy","Classification",{"title":5,"description":41},{"changefreq":54,"priority":55},"weekly",0.7,"terms\u002Fp\u002Fprecision","Q3THh4n7qa1-phXur2M59iNr_jHIDNzKwHGYshNMnno",[59,63,66,69],{"title":50,"path":60,"acronym":6,"category":40,"difficulty":61,"description":62},"\u002Fterms\u002Fa\u002Faccuracy","beginner","Accuracy is the simplest way to score a model — what percentage of predictions were correct.",{"title":51,"path":64,"acronym":6,"category":40,"difficulty":61,"description":65},"\u002Fterms\u002Fc\u002Fclassification","Classification is teaching an AI to sort things into categories. Is this email spam or not? Is this image a cat, dog, or bird?",{"title":49,"path":67,"acronym":6,"category":40,"difficulty":42,"description":68},"\u002Fterms\u002Ff\u002Ff1-score","The F1 Score is the balanced average of precision and recall — a single number that captures both.",{"title":48,"path":70,"acronym":6,"category":40,"difficulty":42,"description":71},"\u002Fterms\u002Fr\u002Frecall","Recall asks: 'Of all the actual YES cases in the world, how many did the AI catch?' High recall means the model finds almost everything it should.",1776518302908]