{"id":178974,"date":"2024-04-18T14:54:34","date_gmt":"2024-04-18T13:54:34","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=178974"},"modified":"2024-02-17T16:35:20","modified_gmt":"2024-02-17T15:35:20","slug":"hyperparameters-what-are-they-what-are-they-used-for","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/hyperparameters-what-are-they-what-are-they-used-for","title":{"rendered":"Hyperparameters: What are they? What are they used for?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"178974\" class=\"elementor elementor-178974\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9774394 e-flex e-con-boxed e-con\" data-id=\"9774394\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2bb8d31 elementor-widget elementor-widget-heading\" data-id=\"2bb8d31\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Machine learning models are powerful tools for solving complex problems, from predicting stock market trends to <a href=\"https:\/\/datascientest.com\/data-science-sante-medecine\">diagnosing diseases<\/a>. However, to get the most out of these models, you need to understand the role of hyperparameters and how to optimise them for better performance.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1956fed elementor-widget elementor-widget-text-editor\" data-id=\"1956fed\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>In this article, we will look at the different types of <strong>hyperparameters<\/strong> and the techniques used to optimise them. We will also discuss the importance of understanding the relationship between hyperparameters and model performance, and the trade-offs that need to be made.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e71deb1 e-flex e-con-boxed e-con\" data-id=\"e71deb1\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a3b2a66 elementor-widget elementor-widget-heading\" data-id=\"a3b2a66\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is a hyperparameter?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b998140 elementor-widget elementor-widget-text-editor\" data-id=\"b998140\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>A hyperparameter is a parameter that is <a href=\"https:\/\/datascientest.com\/en\/data-poisoning-a-threat-to-machine-learning-models\">used to configure a Machine Learning model.<\/a> Unlike model parameters, which are learned from training data, hyperparameters must be defined by the user before training the model.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d251299 elementor-widget elementor-widget-image\" data-id=\"d251299\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image1.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b9f8d3a elementor-widget elementor-widget-text-editor\" data-id=\"b9f8d3a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Hyperparameters<\/strong> are generally chosen according to the desired performance of the model, as well as its characteristics and limitations.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1a651b7 e-flex e-con-boxed e-con\" data-id=\"1a651b7\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-adf67ec elementor-widget elementor-widget-heading\" data-id=\"adf67ec\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why are hyperparameters important?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c59e60e elementor-widget elementor-widget-text-editor\" data-id=\"c59e60e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Hyperparameters<\/strong> are important because they can have a significant impact on model performance. For example, by using inappropriate values, it is then possible to overfit or <a href=\"https:\/\/datascientest.com\/en\/cracking-the-code-on-underfitting-in-machine-learning\">underfit the training data.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-31b92bf elementor-widget elementor-widget-image\" data-id=\"31b92bf\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image2-2.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6e8e04 elementor-widget elementor-widget-text-editor\" data-id=\"d6e8e04\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\">Overfitting<\/a> will occur when the model fits the training data too closely and does not generalise well to the test data. This can lead to a drop in the model&#8217;s performance on the test data.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-144d33e elementor-widget elementor-widget-image\" data-id=\"144d33e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image4-2.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4890f31 elementor-widget elementor-widget-text-editor\" data-id=\"4890f31\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Underfitting<\/strong> occurs when the model does not adapt sufficiently to the training data and fails to capture the complex relationships present in the data. This can lead to a drop in the model&#8217;s performance on the training and test data.<\/p><p>In summary, it is important to choose the <strong>hyperparameters<\/strong> in such a way as to avoid overfitting and underfitting and thus obtain a model that performs well on the test data.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ee7ed96 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"ee7ed96\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Mastering the use of hyperparameters<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-be7de27 e-flex e-con-boxed e-con\" data-id=\"be7de27\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-26b2f72 elementor-widget elementor-widget-heading\" data-id=\"26b2f72\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the differences between Hyperparameters?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-969f9af elementor-widget elementor-widget-text-editor\" data-id=\"969f9af\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>There are several methods for choosing the <strong>hyperparameters<\/strong> of a model. Here are a few examples:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eaf2ecb elementor-widget elementor-widget-heading\" data-id=\"eaf2ecb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Grid search<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-178cacd elementor-widget elementor-widget-text-editor\" data-id=\"178cacd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>This parameter consists of<strong> trying out all the possible combinations of hyperparameters,<\/strong> training a model for each combination, and choosing the combination that gives the best results.<\/p><p>This method can be very efficient, but it can be very time-consuming if you have a lot of hyperparameters and possible values to try. In addition, it can be expensive in terms of computational resources if you need a lot of model training to find the best combinations.<\/p><p>Despite these drawbacks, grid search remains a popular method for choosing hyperparameters and can be very effective in many situations. If you have the time and computational resources, this is an approach to consider <strong>when choosing hyperparameters for your model.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a82320a elementor-widget elementor-widget-heading\" data-id=\"a82320a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Random search<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1702ad5 elementor-widget elementor-widget-text-editor\" data-id=\"1702ad5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Random search involves randomly selecting different combinations of hyperparameters and training a model for each combination. This method can be less efficient than grid search, but the advantage is that it is often <strong>quicker to implement and can give good results in certain situations.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5877cd elementor-widget elementor-widget-heading\" data-id=\"d5877cd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Bayesian optimisation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36e5b3e elementor-widget elementor-widget-text-editor\" data-id=\"36e5b3e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Consists of using a probability distribution on the <strong>hyperparameters<\/strong> and updating this distribution according to the results obtained when training the model. This method can be more efficient than grid search and random search, but it generally requires the use of specialised tools and can be more<strong> complex to implement.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6040c50 elementor-widget elementor-widget-image\" data-id=\"6040c50\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/04\/image3.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1dbe949 elementor-widget elementor-widget-heading\" data-id=\"1dbe949\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Experimentation and trial and error<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df77154 elementor-widget elementor-widget-text-editor\" data-id=\"df77154\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>This involves iteratively trying out different hyperparameter values and adjusting the <strong>hyperparameters<\/strong> according to the results obtained. This method can be useful when you have a good understanding of the hyperparameters and their impact on the model, but it can be less effective than the other methods when you don&#8217;t know which <strong>hyperparameters are the most important.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-581ad13 elementor-widget elementor-widget-text-editor\" data-id=\"581ad13\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Ultimately, the choice of hyperparameters depends on many factors, such as the complexity of the data, the desired performance and the resources available. It is advisable to test several approaches to find the hyperparameters that give the best results for your model.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-67d1392 e-flex e-con-boxed e-con\" data-id=\"67d1392\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6582ace elementor-widget elementor-widget-heading\" data-id=\"6582ace\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dee0ff1 elementor-widget elementor-widget-text-editor\" data-id=\"dee0ff1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Hyperparameters<\/strong> are parameters that are used to<a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\"> configure a Machine Learning model<\/a> and that can have a significant impact on its performance. It is important to choose hyperparameters carefully to avoid overfitting and underfitting and to obtain a high-performance model.<\/p><p>There are several methods for choosing hyperparameters, each with its own advantages and disadvantages. Ultimately, the choice of hyperparameters depends on many factors and may require some <strong>trial and error to find the best values.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2819832 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"2819832\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to use hyperparameters<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-84777de e-flex e-con-boxed e-con\" data-id=\"84777de\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d56d509 elementor-widget elementor-widget-html\" data-id=\"d56d509\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is a hyperparameter?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"A hyperparameter is a parameter that is used to configure a Machine Learning model. Unlike model parameters, which are learned from training data, hyperparameters must be set by the user before training the model begins.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Why are hyperparameters important?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Hyperparameters are important because they can have a significant impact on model performance. For example, by using inappropriate values, it is then possible to overfit or underfit the training data.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Grid search\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"This parameter consists of trying all possible combinations of hyperparameters, training a model for each combination, and choosing the combination that gives the best results. This method can be very effective, but it can be very time-consuming if you have a lot of hyperparameters and possible values to try. In addition, it can be expensive in terms of computational resources if you need a lot of model training to find the best combinations. Despite these drawbacks, grid search remains a popular method for choosing hyperparameters and can be very effective in many situations. If you have the time and computational resources, this is an approach to consider when choosing hyperparameters for your model.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Random search\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Random search involves randomly selecting different combinations of hyperparameters and training a model for each combination. This method can be less efficient than grid search, but the advantage is that it is often quicker to implement and can give good results in some situations.\"\n    }\n  }]\n}\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Machine learning models are powerful tools for solving complex problems, from predicting stock market trends to diagnosing diseases. However, to get the most out of these models, you need to understand the role of hyperparameters and how to optimise them for better performance. In this article, we will look at the different types of hyperparameters [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":169885,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[46],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Hyperparameters: What are they? What are they used for?<\/title>\n<meta name=\"description\" content=\"Machine learning models are powerful tools for solving complex problems, from predicting stock market trends to diagnosing diseases. 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