[{"data":1,"prerenderedAt":73},["ShallowReactive",2],{"term-l\u002Flogistic-regression":3,"related-l\u002Flogistic-regression":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\u002Fl\u002Flogistic-regression.md","Logistic Regression",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",{},"Despite the name, logistic regression isn't about predicting a number, it's about predicting yes or no: spam or not spam, will churn or won't. It takes the same idea as linear regression but squashes the output through a curve so it always comes out as a probability between 0 and 1, like a 73% chance this email is spam.",[11,20,22],{"id":21},"real-talk","Real Talk",[16,24,25],{},"Logistic regression is a classification algorithm that models the probability of a binary, or multiclass, outcome by applying a sigmoid function to a linear combination of input features, producing outputs bounded between 0 and 1. Despite predating deep learning by decades, it remains a fast, interpretable, and hard-to-beat baseline for many classification problems.",[11,27,29],{"id":28},"when-youll-hear-this","When You'll Hear This",[16,31,32],{},"\"Before we build a neural net, let's see how far logistic regression gets us.\" \u002F \"Logistic regression is still winning on this dataset, the extra complexity of the ensemble isn't paying off.\"",{"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","Despite the name, logistic regression isn't about predicting a number, it's about predicting yes or no: spam or not spam, will churn or won't.","intermediate","md","l",{},true,"\u002Fterms\u002Fl\u002Flogistic-regression",[49,50,51,52],"Linear Regression","Classification","Confusion Matrix","Feature Engineering",{"title":5,"description":41},{"changefreq":55,"priority":56},"weekly",0.7,"terms\u002Fl\u002Flogistic-regression","YE6vU1gi_DiYsA8JFymd-RTPACO4ZjAV4khlfxbeyAs",[60,64,67,70],{"title":50,"path":61,"acronym":6,"category":40,"difficulty":62,"description":63},"\u002Fterms\u002Fc\u002Fclassification","beginner","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":62,"description":66},"\u002Fterms\u002Fc\u002Fconfusion-matrix","A confusion matrix is a little table that stops your model from lying to you with a single accuracy number.",{"title":52,"path":68,"acronym":6,"category":40,"difficulty":42,"description":69},"\u002Fterms\u002Ff\u002Ffeature-engineering","Feature engineering is the unglamorous, hugely important work of turning raw, messy real-world data into the specific columns a model can actually learn fr...",{"title":49,"path":71,"acronym":6,"category":40,"difficulty":62,"description":72},"\u002Fterms\u002Fl\u002Flinear-regression","Linear regression is drawing the single straightest line through a scatter of dots that comes closest to all of them at once, then using that line to guess...",1792528097521]