[{"data":1,"prerenderedAt":77},["ShallowReactive",2],{"term-a\u002Faccuracy":3,"related-a\u002Faccuracy":60},{"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":54,"sitemap":55,"stem":58,"subcategory":6,"__hash__":59},"terms\u002Fterms\u002Fa\u002Faccuracy.md","Accuracy",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",{},"Accuracy is the simplest way to score a model — what percentage of predictions were correct. 95% accuracy sounds great, until you realize 95% of your emails are not spam, so a model that never flags anything gets 95% accuracy too. Accuracy lies. Always check precision and recall too.",[11,20,22],{"id":21},"real-talk","Real Talk",[16,24,25],{},"Accuracy is the fraction of correct predictions out of total predictions: (TP + TN) \u002F (TP + TN + FP + FN). It is a useful metric when classes are balanced but misleading for imbalanced datasets. A spam filter with 99% non-spam data can achieve 99% accuracy by predicting 'not spam' for everything.",[11,27,29],{"id":28},"when-youll-hear-this","When You'll Hear This",[16,31,32],{},"\"The model hit 98% accuracy on the test set.\" \u002F \"Accuracy is misleading on this imbalanced dataset.\"",{"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","Accuracy is the simplest way to score a model — what percentage of predictions were correct.","beginner","md","a",{},true,"\u002Fterms\u002Fa\u002Faccuracy",[49,50,51,52,53],"Precision","Recall","F1 Score","Classification","Overfitting",{"title":5,"description":41},{"changefreq":56,"priority":57},"weekly",0.7,"terms\u002Fa\u002Faccuracy","A4EJdvEyP_DwAaxIYUACApeJd2ANl3L9Z-XJdWC6gHs",[61,64,68,71,74],{"title":52,"path":62,"acronym":6,"category":40,"difficulty":42,"description":63},"\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":51,"path":65,"acronym":6,"category":40,"difficulty":66,"description":67},"\u002Fterms\u002Ff\u002Ff1-score","intermediate","The F1 Score is the balanced average of precision and recall — a single number that captures both.",{"title":53,"path":69,"acronym":6,"category":40,"difficulty":66,"description":70},"\u002Fterms\u002Fo\u002Foverfitting","Overfitting is when your model gets TOO good at the training data and becomes useless on new data.",{"title":49,"path":72,"acronym":6,"category":40,"difficulty":66,"description":73},"\u002Fterms\u002Fp\u002Fprecision","Precision asks: 'Of all the times the AI said YES, how often was it actually right?",{"title":50,"path":75,"acronym":6,"category":40,"difficulty":66,"description":76},"\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.",1776518254076]