{"id":187734,"date":"2024-08-13T06:48:00","date_gmt":"2024-08-13T05:48:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=187734"},"modified":"2025-07-15T10:35:34","modified_gmt":"2025-07-15T09:35:34","slug":"all-about-machine-learning-metrics","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-machine-learning-metrics","title":{"rendered":"Machine Learning Metrics: How to Evaluate a Model?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"187734\" class=\"elementor elementor-187734\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0d00be1 e-flex e-con-boxed e-con\" data-id=\"0d00be1\" 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-e572900 elementor-widget elementor-widget-heading\" data-id=\"e572900\" 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><p class=\"elementor-heading-title elementor-size-default\">After developing a Machine Learning model, evaluating its performance is crucial to assessing its effectiveness. To objectively compare multiple models, the use of metrics is indispensable. Understanding and knowing how to use these metrics is essential for developing an effective Machine Learning model. In this article, you will discover the main metrics used to evaluate a Machine Learning model.<\/p>\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-a9b281a e-flex e-con-boxed e-con\" data-id=\"a9b281a\" 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-91603a2 elementor-widget elementor-widget-heading\" data-id=\"91603a2\" 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 metric in Machine Learning?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b9513fe elementor-widget elementor-widget-text-editor\" data-id=\"b9513fe\" 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><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">Machine Learning<\/a> allows computers to learn and make <b>predictions<\/b><span style=\"font-weight: 400;\"> or <\/span><b>decisions based on data<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">There are two types of learning: <a href=\"https:\/\/datascientest.com\/en\/discover-supervised-learning-in-5-questions\">supervised learning<\/a> and <a href=\"https:\/\/datascientest.com\/en\/unsupervised-learning-principle-and-use\">unsupervised learning<\/a>.<\/span><\/p><p><span style=\"font-weight: 400;\">In this article, we will focus on a supervised framework. For more details on the basics of Machine Learning and the difference between these two types of learning, we recommend reading <a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">this article<\/a>, which introduces key Machine Learning concepts useful for understanding the use of metrics in a Machine Learning model.<\/span><\/p><p><span style=\"font-weight: 400;\">A metric is a numerical value that <\/span><b>quantifies the quality of a model\u2019s predictions<\/b><span style=\"font-weight: 400;\">. Its role is essential during all stages of the development of a Machine Learning model as it helps determine if a model meets our expectations. Based on the results obtained, metrics allow for the objective comparison of several models with each other, the selection of the most efficient model, or changes to a model\u2019s <\/span><b>hyperparameters<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">A good grasp of different metrics is essential for deploying an effective model.<\/span><\/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-9c9f60f elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"9c9f60f\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-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 to deploy ML models<\/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-1babcf1 e-flex e-con-boxed e-con\" data-id=\"1babcf1\" 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-d7d7203 elementor-widget elementor-widget-heading\" data-id=\"d7d7203\" 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\">Which metric to choose for my model?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f4fe0d elementor-widget elementor-widget-text-editor\" data-id=\"3f4fe0d\" 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><span style=\"font-weight: 400;\">To <\/span><b>choose the appropriate metric<\/b><span style=\"font-weight: 400;\">, it is important to understand the context of the problem and the model\u2019s objectives. There are many metrics, and we will present a few of them and their advantages.<\/span><\/p><p><span style=\"font-weight: 400;\">In a supervised learning framework, one must first determine the <\/span><b>type of prediction<\/b><span style=\"font-weight: 400;\"> the model must make. If the model must predict a numerical value (e.g., the price of a house), it is a regression problem (e.g., linear regression). Conversely, if the model must predict a <\/span><b>categorical value<\/b><span style=\"font-weight: 400;\"> (e.g., the presence or absence of fraud in a banking transaction), we are in a classification context. The metrics used in regression models are indeed different from those used in classification models.<\/span><\/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-facda5c elementor-widget elementor-widget-heading\" data-id=\"facda5c\" 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-medium\">A. Regression Metrics<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-18a26c6 elementor-widget elementor-widget-text-editor\" data-id=\"18a26c6\" 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><span style=\"font-weight: 400;\">In this article, we will present two of the primary regression metrics: Mean Squared Error (MSE) and Mean Absolute Error (MAE).<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mean Squared Error (MSE) is defined as follows:<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e152319 elementor-widget elementor-widget-shortcode\" data-id=\"e152319\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><math xmlns='http:\/\/www.w3.org\/1998\/Math\/MathML'> <mfrac> <mrow> <mn> 1 <\/mn> <\/mrow> <mrow> <mi> N <\/mi> <\/mrow> <\/mfrac> <munderover> <mrow> <mo> &#x2211; <!-- n-ary summation --> <\/mo> <\/mrow> <mrow> <mi> i <\/mi> <mo> = <\/mo> <mn> 1 <\/mn> <\/mrow> <mrow> <mi> N <\/mi> <\/mrow> <\/munderover> <msup> <mrow> <mrow> <mo> ( <\/mo> <msub> <mrow> <mi> y <\/mi> <\/mrow> <mrow> <mi> i <\/mi> <\/mrow> <\/msub> <mo> - <\/mo> <msub> <mrow> <mover> <mrow> <mi> y <\/mi> <\/mrow> <mrow> <mo> &#x2227; <!-- logical and --> <\/mo> <\/mrow> <\/mover> <\/mrow> <mrow> <mi> i <\/mi> <\/mrow> <\/msub> <mo> ) <\/mo> <\/mrow> <\/mrow> <mrow> <mn> 2 <\/mn> <\/mrow> <\/msup> <\/math><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b8d93b9 elementor-widget elementor-widget-text-editor\" data-id=\"b8d93b9\" 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><span style=\"font-weight: 400;\">where <\/span><b>N<\/b><span style=\"font-weight: 400;\"> is the number of observations, <\/span><b>yi<\/b><span style=\"font-weight: 400;\"> is the actual value, and <\/span><b>\u0177i<\/b><span style=\"font-weight: 400;\"> is the predicted value.<\/span><\/p><p><span style=\"font-weight: 400;\">This metric sums the squares of the differences between actual values and predictions. Mean Squared Error heavily penalizes large differences between the actual value and the prediction, which can be useful in contexts where such differences are particularly undesirable.<\/span><\/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-ca6bb06 elementor-widget elementor-widget-image\" data-id=\"ca6bb06\" 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\" width=\"900\" height=\"514\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest1.jpg\" class=\"attachment-full size-full wp-image-187750\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest1.jpg 900w, https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest1-300x171.jpg 300w, https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest1-768x439.jpg 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\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-7d09014 elementor-widget elementor-widget-text-editor\" data-id=\"7d09014\" 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><span style=\"font-weight: 400;\">You can get more information on Mean Squared Error by reading this article, which details its characteristics and an example of its application.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mean Absolute Error (MAE) is defined as follows:<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-834fd94 elementor-widget elementor-widget-shortcode\" data-id=\"834fd94\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><math xmlns='http:\/\/www.w3.org\/1998\/Math\/MathML'> <mfrac> <mrow> <mn> 1 <\/mn> <\/mrow> <mrow> <mi> N <\/mi> <\/mrow> <\/mfrac> <munderover> <mrow> <mo> &#x2211; <!-- n-ary summation --> <\/mo> <\/mrow> <mrow> <mi> i <\/mi> <mo> = <\/mo> <mn> 1 <\/mn> <\/mrow> <mrow> <mi> N <\/mi> <\/mrow> <\/munderover> <msup> <mrow> <mrow> <mo> | <\/mo> <msub> <mrow> <mi> y <\/mi> <\/mrow> <mrow> <mi> i <\/mi> <\/mrow> <\/msub> <mo> - <\/mo> <msub> <mrow> <mover> <mrow> <mi> y <\/mi> <\/mrow> <mrow> <mo> &#x2227; <!-- logical and --> <\/mo> <\/mrow> <\/mover> <\/mrow> <mrow> <mi> i <\/mi> <\/mrow> <\/msub> <mo> | <\/mo> <\/mrow> <\/mrow>  <\/math><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e9e5db8 elementor-widget elementor-widget-text-editor\" data-id=\"e9e5db8\" 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><span style=\"font-weight: 400;\">where <\/span><b>N<\/b><span style=\"font-weight: 400;\"> is the number of observations, <\/span><b>yi<\/b><span style=\"font-weight: 400;\"> is the actual value, and <\/span><b>\u0177i<\/b><span style=\"font-weight: 400;\"> is the predicted value.<\/span><\/p><p><span style=\"font-weight: 400;\">This metric sums the absolute values of the differences between predictions and actual values.<\/span><\/p><p><span style=\"font-weight: 400;\">Mean Absolute Error is less sensitive to large differences than Mean Squared Error.<\/span><\/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-33e2c65 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"33e2c65\" 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-md\" 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 Machine Learning metrics<\/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<div class=\"elementor-element elementor-element-79973be elementor-widget elementor-widget-heading\" data-id=\"79973be\" 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-medium\">B. Classification Metrics<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d14c7b elementor-widget elementor-widget-text-editor\" data-id=\"4d14c7b\" 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><span style=\"font-weight: 400;\">In a classification framework, the way to evaluate a model\u2019s performance is different. We will present three primary classification metrics: accuracy, precision, and recall.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">To calculate accuracy, simply evaluate the rate of correct predictions relative to the total number of predictions:<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-510ca39 elementor-widget elementor-widget-shortcode\" data-id=\"510ca39\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><math xmlns='http:\/\/www.w3.org\/1998\/Math\/MathML'> <mfrac> <mrow> <mn> <i>Number of correct predictions<\/i> <\/mn> <\/mrow> <mrow> <mi> <i>Total number of predictions<\/i> <\/mi> <\/mrow>  <\/math><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51f6627 elementor-widget elementor-widget-text-editor\" data-id=\"51f6627\" 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 style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">This formula returns a number between <\/span><b>0<\/b><span style=\"font-weight: 400;\"> and <\/span><b>1<\/b><span style=\"font-weight: 400;\">. A score close to <\/span><b>1<\/b><span style=\"font-weight: 400;\"> indicates a very good model, whereas a score close to <\/span><b>0<\/b><span style=\"font-weight: 400;\"> indicates a poor model. This metric is quite intuitive and easy to understand. However, it poorly evaluates the performance of a model based on imbalanced data, or data where <\/span><b>prediction errors<\/b><span style=\"font-weight: 400;\"> do not have the same impact.<\/span><\/p><p style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">To illustrate this concept, let\u2019s take the example of a model that detects the presence of a disease in a patient. If in 90% of the cases, the patient is not sick, the model could systematically predict that the patient is healthy. The accuracy of this model would then be 0.9, which seems to be a very good score. However, two major problems can arise:<\/span><\/p><ul><li style=\"list-style-type: none;\"><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">First, the model would be incapable of detecting the disease in a patient.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\">Secondly, the <b>quality of predictions<\/b><span> is not taken into account. Predicting that a patient is sick when they are not (known as a <\/span><b>false positive<\/b><span>) will not have the same impact as predicting that a patient is not sick when they are (<\/span><b>false negative<\/b><span>).<\/span><\/li><\/ul><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a88bb9 elementor-widget elementor-widget-image\" data-id=\"2a88bb9\" 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\t\t\t<img decoding=\"async\" width=\"900\" height=\"514\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest2.jpg\" class=\"attachment-full size-full wp-image-187766\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest2.jpg 900w, https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest2-300x171.jpg 300w, https:\/\/datascientest.com\/en\/files\/2024\/07\/metrics-machine-learning-datascientest2-768x439.jpg 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\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-e13bf89 elementor-widget elementor-widget-text-editor\" data-id=\"e13bf89\" 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><span style=\"font-weight: 400;\">Accuracy does not allow for nuances between different predictions and does not account for imbalanced data. This is why there are metrics to address this problem:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Precision is defined as follows:<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c58085c elementor-widget__width-initial elementor-widget elementor-widget-shortcode\" data-id=\"c58085c\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><math xmlns='http:\/\/www.w3.org\/1998\/Math\/MathML'> <mfrac> <mrow> <mn> <i>TP<\/i> <\/mn> <\/mrow> <mrow> <mi> <i>TP + FP<\/i> <\/mi> <\/mrow>  <\/math><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2fda357 elementor-widget elementor-widget-text-editor\" data-id=\"2fda357\" 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><span style=\"font-weight: 400;\">where <\/span><b>TP<\/b><span style=\"font-weight: 400;\"> represents the number of True Positives and <\/span><b>FP<\/b><span style=\"font-weight: 400;\"> represents the number of False Positives.<\/span><\/p><p><span style=\"font-weight: 400;\">This metric is useful when the cost of false positives is high.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Similarly, recall is defined as follows:<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21d9d1 elementor-widget elementor-widget-shortcode\" data-id=\"f21d9d1\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><math xmlns='http:\/\/www.w3.org\/1998\/Math\/MathML'> <mfrac> <mrow> <mn> <i>TP<\/i> <\/mn> <\/mrow> <mrow> <mi> <i>TP + FN<\/i> <\/mi> <\/mrow>  <\/math><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c43684d elementor-widget elementor-widget-text-editor\" data-id=\"c43684d\" 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><span style=\"font-weight: 400;\">where <\/span><b>TP<\/b><span style=\"font-weight: 400;\"> represents the number of True Positives and <\/span><b>FN<\/b><span style=\"font-weight: 400;\"> represents the number of False Negatives.<\/span><\/p><p><span style=\"font-weight: 400;\">This metric is useful when the cost of false negatives is high.<\/span><\/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-6300ada e-flex e-con-boxed e-con\" data-id=\"6300ada\" 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-c818772 elementor-widget elementor-widget-heading\" data-id=\"c818772\" 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-353d632 elementor-widget elementor-widget-text-editor\" data-id=\"353d632\" 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><span style=\"font-weight: 400;\">In conclusion, using metrics is essential to <\/span><b>evaluate the performance of a Machine Learning model<\/b><span style=\"font-weight: 400;\">. Choosing the right metric for the model allows for making the correct decisions regarding how to improve it. Depending on the type of model (<\/span><b>classification or regression model<\/b><span style=\"font-weight: 400;\">), context, and data type, certain metrics will be preferable to others. It is important to understand the advantages and disadvantages of each metric to use the one that best fits your problem.<\/span><\/p><p><a href=\"https:\/\/datascientest.com\/en\/how-to-become-a-data-scientist-the-the-best-kept-secrets\"><span style=\"font-weight: 400;\">Data Scientists<\/span><\/a> use metrics to prepare effective Machine Learning models. For this, they use a multitude of mathematical concepts and specific software capable of preparing and analyzing data. As such, training is more than necessary. This is precisely possible with <a href=\"https:\/\/datascientest.com\/en\/\">DataScientest<\/a>. We offer comprehensive training programs in bootcamps, continuous, or alternating formats.<\/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-80b7950 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"80b7950\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\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\">Start training with DataScientest<\/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-e1e4b7e e-flex e-con-boxed e-con\" data-id=\"e1e4b7e\" 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-727e400 elementor-widget elementor-widget-html\" data-id=\"727e400\" 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\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Why is it important to choose the right metrics to evaluate a Machine Learning model?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"It is crucial to choose the right metrics because they determine how the model's performance is evaluated. An inappropriate metric can give a misleading picture of the model's quality.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the main metrics used in Machine Learning?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"The main metrics used in Machine Learning include: \\n - Accuracy: The percentage of correct predictions out of the total predictions. \\n - Precision: The ratio of true positive predictions to the total positive predictions. \\n - Recall: The ratio of true positive predictions to the total actual positive elements.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"How to choose between precision and recall to evaluate a model?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"The choice between precision, recall, and the F1-Score depends on the context and priorities of the model's application: \\n - Precision: Used when the cost of false positives is high. For example, in spam filtering, where it is important to minimize legitimate emails being classified as spam. \\n - Recall: Preferred when the cost of false negatives is high. For example, in medical screening, where it is crucial to identify all positive cases, even if it means more false positives.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\r\n\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>After developing a Machine Learning model, evaluating its performance is crucial to assessing its effectiveness. To objectively compare multiple models, the use of metrics is indispensable. Understanding and knowing how to use these metrics is essential for developing an effective Machine Learning model. In this article, you will discover the main metrics used to evaluate [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":187738,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425,2409],"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>Machine Learning Metrics: How to Evaluate a Model?<\/title>\n<meta name=\"description\" content=\"Metrics in Machine Learning are essential tools for evaluating model performance. 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