{"id":171102,"date":"2023-09-23T20:13:45","date_gmt":"2023-09-23T19:13:45","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=171102"},"modified":"2023-10-30T20:14:11","modified_gmt":"2023-10-30T19:14:11","slug":"confusion-matrix-what-is-it-and-how-do-i-use-it","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/confusion-matrix-what-is-it-and-how-do-i-use-it","title":{"rendered":"Confusion Matrix: what is it and how do I use it?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"171102\" class=\"elementor elementor-171102\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7c30b193 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7c30b193\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-128642e8\" data-id=\"128642e8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-51ec24e0 elementor-widget elementor-widget-heading\" data-id=\"51ec24e0\" 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\">The performance of a Machine Learning algorithm is directly related to its ability to predict an outcome. When comparing the results of an algorithm to reality, a confusion matrix is used. In this article, you will learn how to read this matrix to interpret the results of a classification algorithm.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-19f13d0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"19f13d0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-66ed9c6f\" data-id=\"66ed9c6f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-580e9415 elementor-widget elementor-widget-heading\" data-id=\"580e9415\" 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 confusion matrix?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3dfef5f1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3dfef5f1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-28fc88fd\" data-id=\"28fc88fd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6933d078 elementor-widget elementor-widget-text-editor\" data-id=\"6933d078\" 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\/unlock-your-future-dive-into-machine-learning-engineer-training\">Machine Learning<\/a> involves feeding an algorithm with data so that it can learn to perform a specific task on its own. In classification problems, it predicts outcomes that need to be compared to the ground truth to measure its performance. The <strong>confusion matrix,<\/strong> also known as a contingency table, is commonly used for this purpose.<\/p><p>It not only highlights correct and incorrect predictions but also provides insights into the types of errors made. To <strong>calculate a confusion matrix,<\/strong> you need a test dataset and a validation dataset containing the actual result values.<\/p><p>Each column of the table represents a class predicted by the algorithm, and the rows represent the actual classes.<\/p><p>Results are classified into four categories:<\/p><ol><li><strong>True Positive (TP): T<\/strong>he prediction and the actual value are both positive. Example: A sick person predicted as sick.<\/li><li><strong>True Negative (TN):<\/strong> The prediction and the actual value are both negative. Example: A healthy person predicted as healthy.<\/li><li><strong>False Positive (FP)<\/strong>: The prediction is positive, but the actual value is negative. Example: A healthy person predicted as sick.<\/li><li><strong>False Negative (FN):<\/strong> The prediction is negative, but the actual value is positive. Example: A sick person predicted as healthy.<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-732798ce elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"732798ce\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-756c2e87\" data-id=\"756c2e87\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-21264040 elementor-widget elementor-widget-image\" data-id=\"21264040\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"464\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3_Plan-de-travail-1-1024x594.png\" class=\"attachment-large size-large wp-image-171106\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3_Plan-de-travail-1-1024x594.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3_Plan-de-travail-1-300x174.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3_Plan-de-travail-1-768x446.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3_Plan-de-travail-1-1536x892.png 1536w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3_Plan-de-travail-1-2048x1189.png 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5dcfd853 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5dcfd853\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b1d83c5\" data-id=\"b1d83c5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-70d23537 elementor-widget elementor-widget-text-editor\" data-id=\"70d23537\" 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>Of course, in more complex scenarios, you can add rows and columns to this matrix.<\/p><p>Example: After applying a predictive model, we obtain the following results:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1b495230 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1b495230\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5c08033c\" data-id=\"5c08033c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-23804ad9 elementor-widget elementor-widget-text-editor\" data-id=\"23804ad9\" 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>In general, you always find the correct <strong>predictions on the diagonal. So, here we have:<\/strong><\/p><p>&#8211; 600 individuals classified as belonging to class A out of a total of 2000 individuals, which is quite low.<br \/>&#8211; For individuals in class B, 1200 out of 2000 were correctly identified as belonging to this class.<br \/>&#8211; For individuals in class C, 1600 out of 2000 were correctly identified.<\/p><p>The number of True Positives (TP) is therefore 3400.<\/p><p>To calculate the<strong> number of False Positives (FP), True Negatives (TN), and False Negatives (FN),<\/strong> it&#8217;s not possible to calculate them directly from this table. You would need to break it down into three cases:<\/p><p>1. Individuals in A and not in (B or C)<br \/>2. Individuals in B and not in (A or C)<br \/>3. Individuals in C and not in (A or B)<\/p><p>You can then calculate all the necessary metrics for analyzing this table.<\/p><p>Here are some common metrics derived from this kind of table:<\/p><p>&#8211; Accuracy<br \/>&#8211; Precision<br \/>&#8211; Negative Predictive Value<br \/>&#8211; Specificity<br \/>&#8211; Sensitivity<\/p><p>In practice, there is a very simple way to <a href=\"https:\/\/datascientest.com\/en\/python-programming-for-beginners-episode-3\">access all of these metrics in Python using the `classification_report` function from the `sklearn` library.<\/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-53b23772 elementor-widget elementor-widget-image\" data-id=\"53b23772\" 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\" width=\"800\" height=\"602\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3-02-1024x771.png\" class=\"attachment-large size-large wp-image-171107\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3-02-1024x771.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3-02-300x226.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3-02-768x578.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3-02-1536x1157.png 1536w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Sans-titre-3-02-2048x1542.png 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6859ddc1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6859ddc1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-58613c93\" data-id=\"58613c93\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-68731609 elementor-widget elementor-widget-text-editor\" data-id=\"68731609\" 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><span style=\"color: #000000;\">\u00a0Example:<\/span><\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-513e370f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"513e370f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5f513379\" data-id=\"5f513379\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4ebc6c39 elementor-widget elementor-widget-shortcode\" data-id=\"4ebc6c39\" 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\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6773840f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6773840f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3ba28cc5\" data-id=\"3ba28cc5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-294f0562 elementor-widget elementor-widget-text-editor\" data-id=\"294f0562\" 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; color: #000000;\">This gives us the following result:<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-31ea2d59 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"31ea2d59\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-46c19038\" data-id=\"46c19038\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6346ab81 elementor-widget elementor-widget-image\" data-id=\"6346ab81\" 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\" width=\"800\" height=\"265\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/pasted-image-0-3.png\" class=\"attachment-large size-large wp-image-171108\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/pasted-image-0-3.png 920w, https:\/\/datascientest.com\/en\/files\/2023\/09\/pasted-image-0-3-300x99.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/pasted-image-0-3-768x255.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-73793c4c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"73793c4c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1c81323\" data-id=\"1c81323\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-23c0f992 elementor-widget elementor-widget-heading\" data-id=\"23c0f992\" 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\">Quelles mesures devrions-nous utiliser pour \u00e9valuer nos pr\u00e9dictions ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-73c86986 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"73c86986\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2c499418\" data-id=\"2c499418\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-744b22d8 elementor-widget elementor-widget-text-editor\" data-id=\"744b22d8\" 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>Here, we chose to name the different classes based on the values of <code>y_true<\/code>. We can then observe various metrics that allow us to assess the quality of our predictions, primarily:<\/p><ul><li>Precision<\/li><li>Recall<\/li><li>F1-score<\/li><\/ul><p>Their formulas are respectively:<\/p><p>Precision: Precision = True Positives \/ (True Positives + False Positives)<\/p><p>Recall: Recall = True Positives \/ (True Positives + False Negatives)<\/p><p>F1-score: F1-score = (2 * Precision * Recall) \/ (Precision + Recall)<\/p><p>These metrics provide valuable insights into the <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-ii\">performance of a classification model.<\/a> Precision measures the accuracy of positive predictions, recall measures the ability to correctly identify positive cases, and the F1-score is a balanced measure that combines both precision and recall into a single value.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-793d570 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"793d570\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-41dbef64\" data-id=\"41dbef64\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3d5b7b69 elementor-widget elementor-widget-text-editor\" data-id=\"3d5b7b69\" 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>In our case, we can observe a Precision of 0 for class 1. This is simply because no individuals from real class 1 are predicted as belonging to that class. In contrast, for individuals in class 2, where 2 out of 3 were correctly assigned to class 2, we have a Precision of 0.67.<\/p><p>Precision measures how many of the <strong>predicted positive cases<\/strong> were correct, so when there are no positive predictions for a particular class, the Precision for that class becomes 0. It&#8217;s an important metric for understanding the model&#8217;s ability to avoid false positives for a specific class.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2f7bdcb1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2f7bdcb1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f662445\" data-id=\"f662445\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1d5069f4 elementor-widget elementor-widget-heading\" data-id=\"1d5069f4\" 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\">The differences between micro-betterment and macro-betterment \n\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5116ac3f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5116ac3f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-63c0b9a8\" data-id=\"63c0b9a8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-426609bc elementor-widget elementor-widget-text-editor\" data-id=\"426609bc\" 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>The calculation and interpretation of these averages are slightly different.<\/p><p>Regardless of the metric used, a macro-average calculates an average after calculating the metric independently for each class. In contrast, a <strong>micro-average<\/strong> takes into account the contributions of each class to calculate the average metric.<\/p><p>In a multi-class classification, this approach is often favored when there is suspicion of imbalance between the classes (in terms of sample size or importance).<\/p><p>For example:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6dd23a58 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6dd23a58\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3e11004a\" data-id=\"3e11004a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7246850f elementor-widget elementor-widget-shortcode\" data-id=\"7246850f\" 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\">\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1fe5c86 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1fe5c86\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-64153e3e\" data-id=\"64153e3e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-71186b43 elementor-widget elementor-widget-text-editor\" data-id=\"71186b43\" 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;\">This gives us the following result:<br \/><\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-76f4e839 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"76f4e839\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-48d4bc29\" data-id=\"48d4bc29\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2f7076ee elementor-widget elementor-widget-image\" data-id=\"2f7076ee\" 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\" width=\"512\" height=\"152\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/unnamed-4-2.png\" class=\"attachment-large size-large wp-image-171112\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/unnamed-4-2.png 512w, https:\/\/datascientest.com\/en\/files\/2023\/09\/unnamed-4-2-300x89.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\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\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3c87b21b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3c87b21b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-52011c8\" data-id=\"52011c8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-22dde0a2 elementor-widget elementor-widget-text-editor\" data-id=\"22dde0a2\" 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>You now know how to read and interpret the results of a <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\">classification algorithm<\/a>. If you want to learn more about this topic, you can check out our Data Analyst, Data Scientist, and <a href=\"https:\/\/datascientest.com\/en\/data-management-course\">Data Manager training programs,<\/a> which cover these concepts in more practical cases.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6e45ae4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6e45ae4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3bd1ecb1\" data-id=\"3bd1ecb1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3b6ee1ba elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3b6ee1ba\" 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\/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\">Discover our courses<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The performance of a Machine Learning algorithm is directly related to its ability to predict an outcome. When comparing the results of an algorithm to reality, a confusion matrix is used. In this article, you will learn how to read this matrix to interpret the results of a classification algorithm. What is a confusion matrix? [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":171105,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"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>Confusion Matrix: what is it and how do I use it?<\/title>\n<meta name=\"description\" content=\"Most people think that a Confusion Matrix, under certain circumstances, has the most chances to be the perfect...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/confusion-matrix-what-is-it-and-how-do-i-use-it\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Confusion Matrix: what is it and how do I use it?\" \/>\n<meta property=\"og:description\" content=\"The performance of a Machine Learning algorithm is directly related to its ability to predict an outcome. 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