{"id":3406,"date":"2025-08-15T10:59:22","date_gmt":"2025-08-15T08:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/model-selection\/"},"modified":"2025-11-27T15:20:58","modified_gmt":"2025-11-27T14:20:58","slug":"model-selection","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/model-selection\/","title":{"rendered":"Feature selection algorithms in machine learning"},"content":{"rendered":"<p>Feature selection algorithms help you choose the most relevant variables for building machine learning models.<\/p>\n<section>They are crucial in applications like <a href=\"https:\/\/www.neuraldesigner.com\/solutions\/customer-segmentation\">customer segmentation<\/a> or <a href=\"https:\/\/www.neuraldesigner.com\/solutions\/medical-diagnosis\">medical diagnosis<\/a>, where outcomes depend on complex interactions between many <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables<\/a>.Since analyzing numerous internal and external factors can be difficult, data scientists rely on these algorithms to handle large <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">datasets<\/a> and focus on the most essential features.<\/p>\n<p>In this post, we present the model selection problem and describe the algorithms most commonly used in practice.<\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> implements all the model selection algorithms described here.<\/p>\n<p>To use these algorithms in practice, you can download the <a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">free trial<\/a>.<\/p>\n<h3>Contents<\/h3>\n<ul>\n<li><a href=\"#ProblemFormulation\">Problem formulation<\/a><\/li>\n<li><a href=\"#GrowingInputs\">Growing inputs<\/a><\/li>\n<li><a href=\"#PruningInputs\">Pruning inputs<\/a><\/li>\n<li><a href=\"#GeneticAlgorithm\">Genetic algorithm<\/a><\/li>\n<\/ul>\n<\/section>\n<section>\n<h2>Problem formulation<\/h2>\n<p data-start=\"92\" data-end=\"186\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\">Model selection<\/a> helps you find the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">neural network<\/a> topology that achieves the best generalization.<\/p>\n<p data-start=\"188\" data-end=\"427\">The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#LossIndex\">selection error<\/a>\u00a0is defined as the error of a neural network on new data, and it measures the model\u2019s ability to make accurate predictions in unseen cases.<\/p>\n<p data-start=\"188\" data-end=\"427\">Input selection algorithms aim to identify the optimal subset of inputs.<\/p>\n<p data-start=\"188\" data-end=\"427\">By performing input selection, you can improve prediction quality by extracting the subset of variables that truly influence a given physical, biological, social, or other process.<\/p>\n<\/section>\n<section>\n<h2>Growing inputs<\/h2>\n<p>The growing inputs method starts by calculating every input&#8217;s correlation with every output in the neural network.<\/p>\n<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#GrowingInputs\">growing inputs<\/a> method begins with the most correlated input and continues to add well-correlated variables until the selection error starts increasing.<\/p>\n<\/section>\n<section>\n<h2>Pruning inputs<\/h2>\n<p>The pruning input method also starts by calculating the correlations among every input and output in the neural network.<\/p>\n<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#PrunningInputs\">pruning inputs<\/a> algorithm\u00a0<span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\">begins by considering all variables in the\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\" target=\"_blank\" rel=\"noopener\">dataset<\/a> and then removes the inputs with minimal<\/span>\u00a0correlation with the outputs.<\/p>\n<\/section>\n<section>\n<h2>Genetic algorithm<\/h2>\n<p>A different class of <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#InputsSelection\">input selection<\/a> method is the genetic algorithm.<\/p>\n<p>This stochastic method is based on the mechanics of natural genetics and biological evolution.<\/p>\n<p>The genetic algorithm implemented includes several methods to perform fitness assignment, selection, crossover, and mutation operators.<\/p>\n<p>The following figure shows a simplified flow diagram of the genetic algorithm.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/genetic_algorithm_big.webp\" alt=\"Genetic algorithms is one of the methods of feature algorithms. The image shows a simplified flow diagram.\" width=\"400\" height=\"608\" \/><\/p>\n<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#GeneticAlgorithm\">genetic algorithm<\/a> starts with a population of different subsets of variables.<\/p>\n<p>In every generation, the fitness of every individual in the population is computed as the selection error for that subset of inputs.<\/p>\n<p>Then, the method evolves the population by selecting some individuals to generate the new population, performing a crossover with the selected population, and mutating the offspring generated during the crossover.<\/p>\n<\/section>\n<section>\n<h2>Conclusions<\/h2>\n<p>However,\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\" target=\"_blank\" rel=\"noopener\">model selection algorithms<\/a> are computationally expensive, which is a significant drawback in terms of\u00a0performance.<\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> includes an advanced model selection framework capable of representing very complex data sets.<\/p>\n<p>This system adds significant value to data scientists, providing them with results in a way previously unachievable.<\/p>\n<\/section>\n<section>\n<h2>Related posts<\/h2>\n<\/section>\n","protected":false},"author":13,"featured_media":1894,"template":"","categories":[],"tags":[36],"class_list":["post-3406","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-tutorials"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Feature selection algorithms in machine learning<\/title>\n<meta name=\"description\" content=\"Machine learning involves intricate variable interactions, guiding feature selection, and algorithm choices.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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