{"id":24373,"date":"2026-10-17T16:19:36","date_gmt":"2026-10-17T14:19:36","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/classification-model\/"},"modified":"2026-10-17T16:19:37","modified_gmt":"2026-10-17T14:19:37","slug":"classification-model","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/classification-model\/","title":{"rendered":"Model types: Classification (or pattern recognition)"},"content":{"rendered":"<style id=\"nd-tutorial-series-css\">.ndts{max-width:1000px;margin:0 auto 26px;padding:18px 20px;border:1px solid #d8e2ef;border-radius:14px;background:#f7fafc;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}.ndts-meta{color:#2d799f;font-size:14px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.ndts-intro{margin:8px 0 14px;color:#405361;line-height:1.5}.ndts-progress{display:grid;grid-template-columns:repeat(7,minmax(0,1fr));gap:6px;margin:0;padding:0;list-style:none}.ndts-progress a{display:flex;align-items:center;justify-content:center;min-height:38px;padding:6px;border:1px solid #cbd9e2;border-radius:7px;background:#fff;color:#285c78!important;text-decoration:none!important;font-size:13px;font-weight:700;text-align:center}.ndts-progress a[aria-current=\"step\"]{border-color:#2d799f;background:#2d799f;color:#fff!important}.ndts-controls{display:flex;justify-content:space-between;align-items:center;gap:10px;margin-top:14px;flex-wrap:wrap}.ndts-controls a{color:#1261a0!important;font-weight:700;text-decoration:none!important}.ndts-bottom{max-width:1000px;margin:26px auto;padding:18px 20px;border-top:1px solid #d8e2ef;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}.ndts-finish{margin:0 0 18px;padding:22px;border:1px solid #d8e2ef;border-radius:12px;background:#f6f9fc}.ndts-btn{display:inline-block;margin:6px 8px 0 0;padding:11px 15px;border-radius:7px;background:#1261a0;color:#fff!important;text-decoration:none!important;font-weight:700}@media(max-width:760px){.ndts-progress{grid-template-columns:repeat(2,minmax(0,1fr))}}<\/style><nav class=\"ndts\" aria-label=\"Machine learning tutorial progress\"><div class=\"ndts-meta\">Machine learning tutorial \u00b7 Chapter 1 of 7<\/div><p class=\"ndts-intro\">This page is part of Chapter 1, <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/\">Model types<\/a>. Follow the sequence or use the progress links to open a specific topic. <a href=\"https:\/\/www.neuraldesigner.com\/learning\/machine-learning-tutorial\/\">Tutorial overview<\/a>.<\/p><ol class=\"ndts-progress\"><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/\" aria-current=\"step\" data-tutorial-nav=\"chapter-1\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'1',link_url:this.href});}\">1. Model types<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/\" data-tutorial-nav=\"chapter-2\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'2',link_url:this.href});}\">2. Data set<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\" data-tutorial-nav=\"chapter-3\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'3',link_url:this.href});}\">3. Neural network<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/\" data-tutorial-nav=\"chapter-4\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'4',link_url:this.href});}\">4. Training strategy<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/\" data-tutorial-nav=\"chapter-5\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'5',link_url:this.href});}\">5. Model selection<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\" data-tutorial-nav=\"chapter-6\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'6',link_url:this.href});}\">6. Testing analysis<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/\" data-tutorial-nav=\"chapter-7\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'progress',tutorial_chapter:'7',link_url:this.href});}\">7. Model deployment<\/a><\/li><\/ol><div class=\"ndts-controls\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/\" data-tutorial-nav=\"chapter\">\u2190 Model types<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/machine-learning-tutorial\/\">Overview<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/forecasting-model\/\" data-tutorial-nav=\"next\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'next',tutorial_chapter:'1',link_url:this.href});}\">1.3. Forecasting \u2192<\/a><\/div><\/nav><style>.ndg{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 12px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndg *{box-sizing:border-box}.ndg a{text-decoration:none}.ndg-wrap{width:min(100%,1000px);margin:0 auto}.ndg-lead{font-size:19px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 22px}.ndg-lead a{color:#2d799f;font-weight:600}.ndg-herofig{margin:0 0 30px;text-align:center}.ndg-herofig img{max-width:min(540px,100%);height:auto}.ndg-eyebrow{margin:0 0 14px;text-align:center;color:#2d799f;font-size:13px;font-weight:800;letter-spacing:.14em;text-transform:uppercase}.ndg-toc{list-style:none;display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:12px;margin:0 0 48px;padding:0}.ndg-toc a{display:flex;align-items:center;gap:10px;height:100%;padding:14px 16px;background:#f2f2f2;border-radius:12px;color:#12354b!important;font-size:15px;font-weight:600;line-height:1.25;box-shadow:-8px -8px 16px rgba(255,255,255,.9),8px 8px 16px rgba(30,83,116,.10)}.ndg-toc a:hover{color:#2d799f!important}.ndg-step{display:grid;grid-template-columns:72px 1fr;gap:26px;margin:0 0 34px;padding:34px 38px;background:#f2f2f2;border-radius:22px;box-shadow:-14px -14px 28px rgba(255,255,255,.92),14px 14px 28px rgba(30,83,116,.12);scroll-margin-top:90px}.ndg-ic{width:72px;height:72px;border-radius:50%;background:#e7f1f7;display:flex;align-items:center;justify-content:center;flex:0 0 72px}.ndg-ic img{width:38px!important;height:38px!important;object-fit:contain;margin:0!important;box-shadow:none!important;border-radius:0!important}.ndg-step__body{min-width:0}.ndg-step__body h2{display:flex!important;align-items:center!important;min-height:72px;margin:0 0 16px!important;padding:0!important;color:#001233;font-size:25px;font-weight:700;line-height:1.2}.ndg-step__body h3{margin:26px 0 12px;color:#12354b;font-size:19px;font-weight:600}.ndg-step__body p{margin:0 0 16px;font-size:17px;line-height:1.62;color:#33424f}.ndg-step__body a{color:#2d799f;font-weight:500}.ndg-step__body ul{margin:0 0 16px;padding-left:22px}.ndg-step__body li{margin:7px 0;font-size:16px;line-height:1.55;color:#33424f}.ndg-step__body img{display:block!important;width:auto!important;max-width:min(560px,100%)!important;height:auto!important;margin:22px auto!important;border-radius:12px!important;box-shadow:0 12px 28px rgba(0,18,51,.12)!important}.ndg-step__body figure{margin:22px auto!important;max-width:min(620px,100%)}.ndg-step__body figcaption{text-align:center;color:#6a7884;font-size:13px;margin-top:8px}.ndg-nav{display:flex;justify-content:space-between;gap:16px;margin:12px 0 8px;flex-wrap:wrap}.ndg-nav a{display:inline-flex;align-items:center;padding:13px 24px;border-radius:10px;background:#f2f2f2;color:#2d799f!important;font-weight:800;font-size:15px;box-shadow:-8px -8px 16px rgba(255,255,255,.92),8px 8px 16px rgba(30,83,116,.10)}.ndg-nav a:hover{color:#1f5f80!important}@media(max-width:820px){.ndg-toc{grid-template-columns:1fr}.ndg-step{grid-template-columns:1fr;gap:16px;padding:26px 22px}.ndg-ic{width:56px;height:56px;flex:0 0 56px}.ndg-step__body h2{min-height:0;font-size:22px}}@media(max-width:640px){.ndg{padding:12px 14px}}<\/style><div class=\"ndg\"><div class=\"ndg-wrap\"><div class=\"ndg-step\" id=\"Classification\"><div class=\"ndg-ic\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/classification.svg\" width=\"44\" height=\"44\" alt=\"\" \/><\/div><div class=\"ndg-step__body\"><h2>1.2. Classification (or pattern recognition)<\/h2><p>A classification model assigns each sample to one category out of a fixed set. The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#InputVariables\">inputs<\/a> describe the sample, for example the measurements taken from a tumor or the activity of a customer, and the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#TargetVariables\" target=\"_blank\" rel=\"noopener\">target<\/a> records the category it belongs to.<\/p>\n<p>Rather than returning a bare label, a neural network for classification estimates, for every class, the probability that the sample belongs to it given the values of its inputs. The predicted class is then the most probable one or, in binary problems, the one chosen by a decision threshold. Probabilities say more than labels: they show how confident the model is, and they let you balance false positives against false negatives.<\/p>\n<p>In the following figure, each sample is described by two inputs, <em>x<sub>1<\/sub><\/em> and <em>x<sub>2<\/sub><\/em>, and belongs to one of two classes, drawn as circles and green squares. The model has to find a boundary that separates the two groups.<\/p>\n<p><img loading=\"lazy\" loading=\"lazy\" decoding=\"async\" style=\"max-width: 600px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/classification.webp\" alt=\"Classification problem\" width=\"600\" height=\"400\" \/><\/p>\n<p>As in approximation, what counts is the performance on samples that the model has not seen: a boundary that wraps tightly around every training point usually classifies new data worse than a smoother one.<\/p>\n<p>We can distinguish between two types of classification models:<\/p>\n<ul>\n<li><a href=\"#BinaryClassification\">Binary classification<\/a><\/li>\n<li><a href=\"#MultipleClassification\">Multiple classification<\/a><\/li>\n<\/ul>\n<h3 id=\"BinaryClassification\">Binary classification<\/h3>\n<p>In binary classification, the target variable is usually binary (true or false).<\/p>\n<p>Some examples are to:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/breast-cancer-diagnosis\/\">Diagnose breast cancer from fine-needle aspirate images<\/a>.<\/li>\n\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/employee-attrition\/\">Reduce employee attrition<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/bank-marketing-campaign\/\">Increase the conversion rate of telemarketing campaigns in banks<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/credit-risk-management\/\">Predict the probability of default of credit card clients<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/tree-wilt-detection\/\">Detect diseased trees from remote sensing data<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/blood-donors-targeting\/\">Target donors in blood donation campaigns<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/use-cases\/medical-diagnosis\/\">Diagnose acute inflammations of the urinary bladder<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/telecommunications-churn\/\">Predict customer churn in telecommunications companies<\/a>.<\/li>\n<\/ul>\n<h3 id=\"MultipleClassification\">Multiple classification<\/h3>\n<p>In multiple classifications, the target variable is usually nominal (class_1, class_2, or class_3).<\/p>\n<p>Some examples are the following:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/iris-flowers-classification\/\">Classify iris flowers from sepal and petal dimensions (setosa, versicolor or virginica)<\/a>.<\/li>\n<li><a href=\"https:\/\/www.neuraldesigner.com\/use-cases\/activity-recognition\/\">Recognize human activity on smartphones (walking, sitting, standing&#8230;)<\/a>.<\/li>\n<\/ul>\n<\/div><\/div><p class=\"ndg-eyebrow\">All model types<\/p><ul class=\"ndg-toc\"><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/approximation-model\/\">1.1. Approximation (or function regression)<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/classification-model\/\" aria-current=\"page\">1.2. Classification (or pattern recognition)<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/forecasting-model\/\">1.3. Forecasting (or time series prediction)<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/anomaly-detection-model\/\">1.4. Anomaly detection<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/text-classification-model\/\">1.5. Text classification<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/image-classification-model\/\">1.6. Image classification model<\/a><\/li><\/ul><\/div><\/div><nav class=\"ndts-bottom\" aria-label=\"Tutorial chapter navigation\"><div class=\"ndts-controls\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/approximation-model\/\" data-tutorial-nav=\"previous\">\u2190 1.1. Approximation<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/\">All model types<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/forecasting-model\/\" data-tutorial-nav=\"next\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'next',tutorial_chapter:'1',link_url:this.href});}\">1.3. Forecasting \u2192<\/a><\/div><\/nav>","protected":false},"author":122,"featured_media":2702,"template":"","categories":[30],"tags":[36],"class_list":["post-24373","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-tutorials","tag-tutorials"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Classification Models (Pattern Recognition) - Neural Designer<\/title>\n<meta name=\"description\" content=\"Learn how classification models estimate class probabilities, how binary and multiple classification differ, and see examples built with Neural Designer.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/classification-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Classification Models (Pattern Recognition) - 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