{"id":3533,"date":"2025-11-25T11:12:58","date_gmt":"2025-11-25T10:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/model-selection\/"},"modified":"2026-08-28T12:49:21","modified_gmt":"2026-08-28T10:49:21","slug":"model-selection","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/","title":{"rendered":"Machine learning tutorial: Model selection"},"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 5 of 7<\/div><p class=\"ndts-intro\">You can read this chapter independently. If this is your first visit, open the overview or start with Chapter 1. <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\/\" 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\/\" aria-current=\"step\" 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\/training-strategy\/\" data-tutorial-nav=\"previous\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'previous',tutorial_chapter:'5',link_url:this.href});}\">\u2190 Training strategy<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/machine-learning-tutorial\/\">Overview<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\" data-tutorial-nav=\"next\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'next',tutorial_chapter:'5',link_url:this.href});}\">Testing analysis \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 24px}.ndg-lead a{color:#2d799f;font-weight:600}.ndg-herofig{margin:0 0 30px;text-align:center}.ndg-herofig img{max-width:min(440px,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;counter-reset:s;display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:12px;margin:0 0 48px;padding:0}.ndg-toc li{counter-increment:s}.ndg-toc a{display:flex;align-items:center;gap:12px;height:100%;padding:13px 16px;background:#f2f2f2;border-radius:12px;color:#12354b!important;font-size:14.5px;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-toc a:before{content:counter(s);display:flex;align-items:center;justify-content:center;width:26px;height:26px;flex:0 0 26px;border-radius:50%;background:#56a1c8;color:#fff;font-size:13px;font-weight:800}.ndg-step{display:grid;grid-template-columns:64px 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-step__no{display:flex;align-items:center;justify-content:center;width:64px;height:64px;border-radius:50%;background:linear-gradient(135deg,#56a1c8 0%,#245e80 100%);color:#fff;font-size:25px;font-weight:800;flex:0 0 64px}.ndg-step__body{min-width:0}.ndg-step__body h2{margin:8px 0 16px;color:#001233;font-size:25px;font-weight:700;line-height:1.2}.ndg-step__body h3{margin:28px 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,.ndg-step__body ol{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:not([src$=\".svg\"]):not([data-src$=\".svg\"]){display:block!important;width:auto!important;max-width:min(580px,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 img[src$=\".svg\"],.ndg-step__body img[data-src$=\".svg\"]{display:block;margin:18px auto;max-width:min(520px,100%);height:auto}.ndg-step__body table{border-collapse:separate;border-spacing:0;margin:20px 0;font-size:15px;width:auto;max-width:100%;background:#fbfcfd;border-radius:12px;overflow:hidden;box-shadow:0 10px 24px rgba(0,18,51,.08)}.ndg-step__body th,.ndg-step__body td{padding:11px 18px;border-bottom:1px solid #e6ecf0;text-align:left}.ndg-step__body th{background:#e9f1f6;color:#12354b;font-weight:600}.ndg-step__body td{color:#33424f}.ndg-step__body pre{margin:22px 0;padding:22px 24px;background:#0b1830;color:#e6eef5;border-radius:14px;overflow-x:auto;font-family:Consolas,Menlo,\"Liberation Mono\",monospace;font-size:13.5px;line-height:1.55;white-space:pre}.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-step__no{width:52px;height:52px;flex:0 0 52px;font-size:22px}.ndg-step__body h2{font-size:22px}}@media(max-width:640px){.ndg{padding:12px 14px}}<\/style><div class=\"ndg\"><div class=\"ndg-wrap\"><div class=\"ndg-lead\"><p>Model selection searches for the neural network architecture with the best generalization properties.<\/p>\n<p>That is, the process that minimizes the error on the selected instances of the data set (the selection error).<\/p>\n<p>There are two families of model selection algorithms in machine learning:<\/p>\n<\/div><div class=\"ndg-herofig\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/model_selection.svg\" width=\"53\" height=\"53\"  alt=\"Model Selection\"\/><\/div><div class=\"ndg-step\" id=\"NeuronsSelection\"><div class=\"ndg-step__no\">1<\/div><div class=\"ndg-step__body\"><h2>Neuron selection<\/h2><p>Two frequent problems in designing a neural network are called underfitting and overfitting.<\/p>\n<p>The best generalization is achieved using a model with the most appropriate complexity to produce a good data fit.<\/p>\n<h3>Underfitting and overfitting<\/h3>\n<p>To illustrate underfitting and overfitting, consider the following data set. It consists of data from a sine function to which white noise has been added.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/sinus.webp\"  alt=\"Sinus\"\/><\/p>\n<p>The best generalization is achieved using a model whose complexity is the most appropriate to produce an adequate data fit. In this case, we use a neural network with one input (x), three hidden neurons, and one output (y).<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/correct-fitting.webp\"  alt=\"Correct Fitting\"\/><\/p>\n<p>In this way, underfitting is the effect of a selection error increasing due to a too-simple model. Here, we have used one hidden neuron.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/underfitting.webp\"  alt=\"Underfitting\"\/><\/p>\n<p>On the contrary, overfitting is the effect of a selection error increasing due to a complex model. In this case, we have used 10 hidden neurons.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/overfitting.webp\"  alt=\"Overfitting\"\/><\/p>\n<p>The error of a neural network on the training instances of the data set is called the training error. Similarly, the error on the selected instances is called the selection error.<\/p>\n<p>The training error measures the ability of the neural network to fit the data it sees. However, the selection error measures the ability of the neural network to generalize to new data.<\/p>\n<p>The following figure shows the training (blue) and selection (orange) errors as a function of the neural network complexity, represented by the number of hidden neurons.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/order-selection.webp\"  alt=\"Order Selection\"\/><\/p>\n<p>As shown, increasing hidden neurons reduces training error, but both very small and very large networks lead to high selection error.<\/p>\n<p>The first case indicates underfitting, while the second indicates overfitting.<\/p>\n<p>Here, the best generalization occurs with four hidden neurons, where the selection error reaches its minimum.<\/p>\n<p>Neuron selection algorithms find the neural network&#8217;s complexity that yields the best generalization properties.<\/p>\n<p>Growing neurons is the most common neuron selection method.<\/p>\n<h3 id=\"GrowingNeurons\">Growing neurons<\/h3>\n<p>This algorithm begins with a small network and gradually adds neurons until a stopping criterion is reached.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/growing_neuron.svg\" alt=\"Growing neuron\" width=\"150\" \/><\/p>\n<p>The algorithm returns the neural network with the optimal number of neurons obtained.<\/p>\n<\/div><\/div><div class=\"ndg-step\" id=\"InputsSelection\"><div class=\"ndg-step__no\">2<\/div><div class=\"ndg-step__body\"><h2>Input selection<\/h2><p>Which features should you use to create a predictive model? This is a difficult question that may require in-depth knowledge of the problem domain.<\/p>\n<p>Input selection algorithms automatically extract those features in the data set that provide the best generalization capabilities.<br \/>\nThey search for the subset of inputs that minimizes the selection error.<\/p>\n<p>The most common input selection algorithms are:<\/p>\n<ul>\n<li><a href=\"#GrowingInputs\">Growing inputs<\/a>.<\/li>\n<li><a href=\"#PrunningInputs\">Pruning inputs<\/a>.<\/li>\n<li><a href=\"#GeneticAlgorithm\">Genetic algorithm<\/a>.<\/li>\n<\/ul>\n<h3 id=\"GrowingInputs\">Growing inputs<\/h3>\n<p>The growing inputs method calculates the correlation of every input with every output in the data set.<\/p>\n<p>It starts with a neural network that only contains the most correlated input and calculates the selection error for that model.<\/p>\n<p>It continues to add the most correlated variables until the selection error increases.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/growing_input.svg\" alt=\"Growing input\" width=\"150\" \/><\/p>\n<p>The algorithm returns the neural network with the optimal subset of inputs found.<\/p>\n<h3 id=\"PruningInputs\">Pruning inputs<\/h3>\n<p>The pruning inputs method starts with all the inputs in the data set.<\/p>\n<p>It keeps removing those inputs with the smallest correlation with the outputs until the selection error increases.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/pruning_input.svg\" alt=\"Pruning input\" width=\"150\" \/><\/p>\n<h3 id=\"GeneticAlgorithm\">Genetic algorithm<\/h3>\n<p>A different class of input selection method is the genetic algorithm.<\/p>\n<p>This is a stochastic method based on natural genetics and biological evolution mechanics.<\/p>\n<p>Genetic algorithms usually include fitness assignment, selection, crossover, and mutation operators.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/genetic_algorithm.webp\" alt=\"genetic algorithm feature selection\" width=\"300\" \/><\/p>\n<p>You can find more information about this topic in the <a href=\"https:\/\/www.neuraldesigner.com\/blog\/genetic_algorithms_for_feature_selection\/\">Genetic algorithms for feature selection<\/a> post on our blog.<\/p>\n<\/div><\/div><\/div><\/div><nav class=\"ndts-bottom\" aria-label=\"Tutorial chapter navigation\"><div class=\"ndts-controls\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\/\" data-tutorial-nav=\"previous\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'previous',tutorial_chapter:'5',link_url:this.href});}\">\u2190 Training strategy<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/machine-learning-tutorial\/\">All chapters<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\" data-tutorial-nav=\"next\" onclick=\"if(typeof gtag==='function'){gtag('event','tutorial_nav_click',{tutorial_action:'next',tutorial_chapter:'5',link_url:this.href});}\">Testing analysis \u2192<\/a><\/div><\/nav>","protected":false},"author":122,"featured_media":1903,"template":"","categories":[30],"tags":[36],"class_list":["post-3533","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>Machine Learning Tutorial: Model Selection - Neural Designer<\/title>\n<meta name=\"description\" content=\"Learn how input selection and neuron selection help identify a simpler, better-performing machine learning model.\" \/>\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\/model-selection\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Neural networks tutorial: Model selection\" \/>\n<meta property=\"og:description\" content=\"This tutorial describes different techniques for improving the generalization performance of a neural network.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/\" \/>\n<meta property=\"og:site_name\" content=\"Neural Designer\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-28T10:49:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"607\" \/>\n\t<meta property=\"og:image:height\" content=\"524\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Neural networks tutorial: Model selection\" \/>\n<meta name=\"twitter:description\" content=\"This tutorial describes different techniques for improving the generalization performance of a neural network.\" \/>\n<meta name=\"twitter:site\" content=\"@NeuralDesigner\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/\",\"name\":\"Machine Learning Tutorial: Model Selection - Neural Designer\",\"isPartOf\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp\",\"datePublished\":\"2025-11-25T10:12:58+00:00\",\"dateModified\":\"2026-08-28T10:49:21+00:00\",\"description\":\"Learn how input selection and neuron selection help identify a simpler, better-performing machine learning model.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#primaryimage\",\"url\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp\",\"contentUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp\",\"width\":607,\"height\":524,\"caption\":\"Hepatitis C (HCV) model graph\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.neuraldesigner.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning\",\"item\":\"https:\/\/www.neuraldesigner.com\/learning\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Machine learning tutorial: Model selection\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#website\",\"url\":\"https:\/\/www.neuraldesigner.com\/\",\"name\":\"Neural Designer\",\"description\":\"No-code AI Platform\",\"publisher\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.neuraldesigner.com\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#organization\",\"name\":\"Neural Designer\",\"url\":\"https:\/\/www.neuraldesigner.com\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png\",\"contentUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png\",\"width\":1024,\"height\":223,\"caption\":\"Neural Designer\"},\"image\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/x.com\/NeuralDesigner\",\"https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Machine Learning Tutorial: Model Selection - Neural Designer","description":"Learn how input selection and neuron selection help identify a simpler, better-performing machine learning model.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/","og_locale":"en_US","og_type":"article","og_title":"Neural networks tutorial: Model selection","og_description":"This tutorial describes different techniques for improving the generalization performance of a neural network.","og_url":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/","og_site_name":"Neural Designer","article_modified_time":"2026-08-28T10:49:21+00:00","og_image":[{"width":607,"height":524,"url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp","type":"image\/webp"}],"twitter_card":"summary_large_image","twitter_title":"Neural networks tutorial: Model selection","twitter_description":"This tutorial describes different techniques for improving the generalization performance of a neural network.","twitter_site":"@NeuralDesigner","twitter_misc":{"Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/","url":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/","name":"Machine Learning Tutorial: Model Selection - Neural Designer","isPartOf":{"@id":"https:\/\/www.neuraldesigner.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#primaryimage"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#primaryimage"},"thumbnailUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp","datePublished":"2025-11-25T10:12:58+00:00","dateModified":"2026-08-28T10:49:21+00:00","description":"Learn how input selection and neuron selection help identify a simpler, better-performing machine learning model.","breadcrumb":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#primaryimage","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/model-graph-hcv.webp","width":607,"height":524,"caption":"Hepatitis C (HCV) model graph"},{"@type":"BreadcrumbList","@id":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.neuraldesigner.com\/"},{"@type":"ListItem","position":2,"name":"Learning","item":"https:\/\/www.neuraldesigner.com\/learning\/"},{"@type":"ListItem","position":3,"name":"Machine learning tutorial: Model selection"}]},{"@type":"WebSite","@id":"https:\/\/www.neuraldesigner.com\/#website","url":"https:\/\/www.neuraldesigner.com\/","name":"Neural Designer","description":"No-code AI Platform","publisher":{"@id":"https:\/\/www.neuraldesigner.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.neuraldesigner.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.neuraldesigner.com\/#organization","name":"Neural Designer","url":"https:\/\/www.neuraldesigner.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","width":1024,"height":223,"caption":"Neural Designer"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/NeuralDesigner","https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/"]}]}},"_links":{"self":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3533","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning"}],"about":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/types\/learning"}],"author":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/users\/122"}],"version-history":[{"count":7,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3533\/revisions"}],"predecessor-version":[{"id":23797,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3533\/revisions\/23797"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media\/1903"}],"wp:attachment":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media?parent=3533"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/categories?post=3533"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/tags?post=3533"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}