[{"data":1,"prerenderedAt":73},["ShallowReactive",2],{"term-r\u002Frecall":3,"related-r\u002Frecall":59},{"id":4,"title":5,"acronym":6,"body":7,"category":40,"description":41,"difficulty":42,"extension":43,"letter":44,"meta":45,"navigation":46,"path":47,"related":48,"seo":53,"sitemap":54,"stem":57,"subcategory":6,"__hash__":58},"terms\u002Fterms\u002Fr\u002Frecall.md","Recall",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",{},"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. A cancer screening model should have very high recall — missing a real case is catastrophic. The tradeoff: to catch everything, you might also flag a lot of false positives.",[11,20,22],{"id":21},"real-talk","Real Talk",[16,24,25],{},"Recall (sensitivity) is TP \u002F (TP + FN) — the fraction of actual positives correctly identified. High recall indicates low false negative rate. Recall is prioritized when missing a positive is costly (e.g., disease detection, security threats). The precision-recall tradeoff is tuned via classification threshold.",[11,27,29],{"id":28},"when-youll-hear-this","When You'll Hear This",[16,31,32],{},"\"Recall is more important here — missing a real threat is dangerous.\" \u002F \"We sacrificed precision to maximize 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","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.","intermediate","md","r",{},true,"\u002Fterms\u002Fr\u002Frecall",[49,50,51,52],"Precision","F1 Score","Accuracy","Classification",{"title":5,"description":41},{"changefreq":55,"priority":56},"weekly",0.7,"terms\u002Fr\u002Frecall","yibg9HdV7YCcoLZFRuiHTtkdDQmpW9cNeGlxv7ldjqY",[60,64,67,70],{"title":51,"path":61,"acronym":6,"category":40,"difficulty":62,"description":63},"\u002Fterms\u002Fa\u002Faccuracy","beginner","Accuracy is the simplest way to score a model — what percentage of predictions were correct.",{"title":52,"path":65,"acronym":6,"category":40,"difficulty":62,"description":66},"\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":50,"path":68,"acronym":6,"category":40,"difficulty":42,"description":69},"\u002Fterms\u002Ff\u002Ff1-score","The F1 Score is the balanced average of precision and recall — a single number that captures both.",{"title":49,"path":71,"acronym":6,"category":40,"difficulty":42,"description":72},"\u002Fterms\u002Fp\u002Fprecision","Precision asks: 'Of all the times the AI said YES, how often was it actually right?",1776518306586]