{"id":188704,"date":"2024-09-13T06:30:00","date_gmt":"2024-09-13T05:30:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=188704"},"modified":"2024-09-06T10:54:01","modified_gmt":"2024-09-06T09:54:01","slug":"all-about-naive-bayes-classifier","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier","title":{"rendered":"Naive Bayes Classifier: Theory and Application"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"188704\" class=\"elementor elementor-188704\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-837168f e-flex e-con-boxed e-con\" data-id=\"837168f\" 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-ae35acc elementor-widget elementor-widget-heading\" data-id=\"ae35acc\" 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\">The Naive Bayes classifier is a classification technique based on Bayes' theorem, with the na\u00efve assumption of independence among predictors. Despite its simplicity, the Naive Bayes classifier has demonstrated its effectiveness in various application areas, including spam filtering, sentiment analysis, and document classification.<\/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-8cf49fc e-flex e-con-boxed e-con\" data-id=\"8cf49fc\" 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-f847e48 elementor-widget elementor-widget-heading\" data-id=\"f847e48\" 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 the Theory Behind the Naive Bayes Classifier?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fa6047 elementor-widget elementor-widget-text-editor\" data-id=\"3fa6047\" 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><span style=\"font-weight: 400;\">The Naive Bayes classifier leverages Bayes&#8217; theorem, which describes the probability of an event based on prior knowledge of conditions that may be related to the event. The formula for Bayes&#8217; theorem is:<\/span><\/p><p style=\"padding-left: 40px;\"><b>P(A|B) = P(B|A) * P(A) \/ P(B)<\/b><\/p><p><span style=\"font-weight: 400;\">where:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(A|B) is the probability of event A given that event B is true.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(B|A) is the probability of event B given that event A is true.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(A) and P(B) are the independent probabilities of events A and B, respectively.<\/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-d7736c2 elementor-widget elementor-widget-text-editor\" data-id=\"d7736c2\" 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 href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\">classification contexts<\/a>, A represents a specific class, and B represents a set of features (or attributes). The Naive Bayes classifier calculates the probability that an example belongs to a given class, assuming that all features are independent of each other.<\/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-a1a3250 elementor-widget elementor-widget-image\" data-id=\"a1a3250\" 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=\"572\" height=\"450\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier1.jpg\" class=\"attachment-full size-full wp-image-188713\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier1.jpg 572w, https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier1-300x236.jpg 300w\" sizes=\"(max-width: 572px) 100vw, 572px\" \/>\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-23abeaf elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"23abeaf\" 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\">Find a course<\/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-e3b8250 e-flex e-con-boxed e-con\" data-id=\"e3b8250\" 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-8e4de76 elementor-widget elementor-widget-heading\" data-id=\"8e4de76\" 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 Types of Naive Bayes Classifiers?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc0c829 elementor-widget elementor-widget-heading\" data-id=\"bc0c829\" 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\">Gaussian Naive Bayes Classifier<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc25524 elementor-widget elementor-widget-text-editor\" data-id=\"fc25524\" 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;\">The Gaussian Naive Bayes classifier is used when the attributes are continuous and follow a normal distribution. This type of classifier is particularly useful when dealing with continuous variables that can be modeled by a Gaussian distribution. The underlying assumption is that the data for each class follow a normal (or Gaussian) distribution.<\/span><\/p><p><span style=\"font-weight: 400;\">Formally, if we have a continuous variable x and a class c, then the conditional probability P(x|c) is given by the probability density function of the normal distribution:<\/span><\/p><p style=\"padding-left: 40px;\"><b>P(x|c) = (1 \/ \u221a(2\u03c0\u03c3\u00b2_c)) * exp(- (x \u2013 \u03bc_c)\u00b2 \/ (2\u03c3\u00b2_c))<\/b><\/p><p><span style=\"font-weight: 400;\">where <\/span><b>\u03bc_c<\/b><span style=\"font-weight: 400;\"> is the mean of the values for class c, and <\/span><b>\u03c3\u00b2_c<\/b><span style=\"font-weight: 400;\"> is the variance of the values for class c. The Gaussian Naive Bayes classifier is often used in applications such as <a href=\"https:\/\/datascientest.com\/en\/facial-recognition-how-it-works-2\">pattern recognition<\/a> and <a href=\"https:\/\/datascientest.com\/en\/image-processing-fundamental-principles-and-practical-uses\">image classification<\/a>.<\/span><\/p><p><span style=\"text-decoration: underline;\"><b>Example<\/b><\/span><\/p><p><span style=\"font-weight: 400;\">Suppose we have two classes c1 and c2 with the following parameters:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">For c1: \u03bc_c1 = 5 and \u03c3\u00b2_c1 = 1<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">For c2: \u03bc_c2 = 10 and \u03c3\u00b2_c2 = 2<\/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-83dc7df elementor-widget elementor-widget-text-editor\" data-id=\"83dc7df\" 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;\">If we want to classify a new observation x = 6, we calculate P(x|c1) and P(x|c2) and compare these values.<\/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-dd43a51 elementor-widget elementor-widget-heading\" data-id=\"dd43a51\" 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\">Multinomial Naive Bayes Classifier<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d661696 elementor-widget elementor-widget-text-editor\" data-id=\"d661696\" 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;\">The Multinomial Naive Bayes classifier is often used for document classification when the data consists of word frequencies. This model is well-suited for discrete data, such as word counts or events. It assumes that the features follow a multinomial distribution, which is suitable for text classification tasks where the features are word occurrences.<\/span><\/p><p><span style=\"font-weight: 400;\">Formally, the conditional probability P(x|c) for a feature x and a class c is given by:<\/span><\/p><p style=\"padding-left: 40px;\"><b>P(x|c) = (n_x,c + \u03b1) \/ (N_c + \u03b1N)<\/b><\/p><p><span style=\"font-weight: 400;\">where <\/span><b>n_x,c<\/b><span style=\"font-weight: 400;\"> is the number of times word x appears in documents of class c, <\/span><b>N_c<\/b><span style=\"font-weight: 400;\"> is the total number of words in documents of class c, <\/span><b>\u03b1<\/b><span style=\"font-weight: 400;\"> is a Laplace smoothing parameter, and <\/span><b>N<\/b><span style=\"font-weight: 400;\"> is the total number of distinct words. Laplace smoothing is used to <\/span><b>handle the issue of zero probabilities<\/b><span style=\"font-weight: 400;\"> for words that do not appear in the training documents.<\/span><\/p><p><span style=\"text-decoration: underline;\"><b>Example<\/b><\/span><\/p><p><span style=\"font-weight: 400;\">Suppose we have two classes, &#8216;sport&#8217; and &#8216;politics&#8217;, and we want to classify the word &#8216;match&#8217;. We have the following counts:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">&#8216;match&#8217; appears 50 times in sport documents and 5 times in politics documents.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The total number of words in the sport class is 1000 and in the politics class is 800.<\/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-b0632d3 elementor-widget elementor-widget-text-editor\" data-id=\"b0632d3\" 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;\">With a Laplace smoothing \u03b1 = 1, we calculate:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(&#8216;match&#8217;|&#8217;sport&#8217;) = (50 + 1) \/ (1000 + 1 * 1000)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(&#8216;match&#8217;|&#8217;politics&#8217;) = (5 + 1) \/ (800 + 1 * 1000)<\/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-9c2ce8a elementor-widget elementor-widget-text-editor\" data-id=\"9c2ce8a\" 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;\">We use these probabilities to classify a new document containing the word &#8216;match&#8217;.<\/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-a0978f0 elementor-widget elementor-widget-image\" data-id=\"a0978f0\" 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=\"519\" height=\"451\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier2.jpg\" class=\"attachment-full size-full wp-image-188720\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier2.jpg 519w, https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier2-300x261.jpg 300w\" sizes=\"(max-width: 519px) 100vw, 519px\" \/>\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-447620a elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"447620a\" 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 all about the Naive Bayes classifier<\/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-6da345e elementor-widget elementor-widget-heading\" data-id=\"6da345e\" 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\">Bernoulli Naive Bayes Classifier<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e1b6643 elementor-widget elementor-widget-text-editor\" data-id=\"e1b6643\" 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;\">The Bernoulli Naive Bayes classifier is suitable for binary variables (presence or absence of a feature). This model is primarily used for text classification tasks where features are binary indicators (0 or 1) representing the presence or absence of a particular word.<\/span><\/p><p><span style=\"font-weight: 400;\">In this model, the conditional probability P(x|c) is calculated based on the presence or absence of the feature:<\/span><\/p><p style=\"padding-left: 40px;\"><b>P(x_i = 1 | c) = (n_i,c + \u03b1) \/ (N_c + 2\u03b1)<\/b><\/p><p style=\"padding-left: 40px;\"><b>P(x_i = 0 | c) = 1 \u2013 P(x_i = 1 | c)<\/b><\/p><p><span style=\"font-weight: 400;\">where <\/span><b>n_i,c<\/b><span style=\"font-weight: 400;\"> is the number of documents in class c in which feature <\/span><b>x_i<\/b><span style=\"font-weight: 400;\"> is present, and <\/span><b>N_c<\/b><span style=\"font-weight: 400;\"> is the total number of documents in class c. The smoothing parameter \u03b1 is used to avoid zero probabilities.<\/span><\/p><p><span style=\"text-decoration: underline;\"><b>Example<\/b><\/span><\/p><p><span style=\"font-weight: 400;\">Suppose we have two classes, &#8216;spam&#8217; and &#8216;non-spam&#8217;, and we want to classify the occurrence of the word &#8216;free&#8217;.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">&#8216;free&#8217; appears in 70 out of 100 spam documents and in 20 out of 100 non-spam documents.<\/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-61938e0 elementor-widget elementor-widget-text-editor\" data-id=\"61938e0\" 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;\">With a Laplace smoothing \u03b1 = 1, we calculate:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(&#8216;free&#8217; = 1|&#8217;spam&#8217;) = (70 + 1) \/ (100 + 2 * 1)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(&#8216;free&#8217; = 0|&#8217;spam&#8217;) = 1 \u2013 P(&#8216;free&#8217; = 1|&#8217;spam&#8217;)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(&#8216;free&#8217; = 1|&#8217;non-spam&#8217;) = (20 + 1) \/ (100 + 2 * 1)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">P(&#8216;free&#8217; = 0|&#8217;non-spam&#8217;) = 1 \u2013 P(&#8216;free&#8217; = 1|&#8217;non-spam&#8217;)<\/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-4e2d832 elementor-widget elementor-widget-text-editor\" data-id=\"4e2d832\" 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;\">We use these probabilities to classify a new document based on the presence or absence of the word &#8216;free&#8217;.<\/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-3720f5a e-flex e-con-boxed e-con\" data-id=\"3720f5a\" 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-9b644e1 elementor-widget elementor-widget-heading\" data-id=\"9b644e1\" 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 Practical Applications?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a6624e elementor-widget elementor-widget-text-editor\" data-id=\"2a6624e\" 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;\">The Naive Bayes classifier is used in numerous domains, including:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b><a href=\"https:\/\/datascientest.com\/en\/data-poisoning-a-threat-to-machine-learning-models\">Spam Filtering<\/a>:<\/b><span style=\"font-weight: 400;\"> Identifying unwanted emails. By using features of the email, such as the frequency of certain words, the Naive Bayes classifier can determine the probability that an email is spam or not.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b><a href=\"https:\/\/datascientest.com\/en\/sentiment-analysis-harnessing-the-power-of-machine-learning\">Sentiment Analysis<\/a>:<\/b><span style=\"font-weight: 400;\"> Determining the sentiment expressed in a text. The classifier can be used to assess whether the sentiments expressed in product reviews, social media comments, or other texts are positive, negative, or neutral.<\/span><\/li><li><b><a href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\">Document Classification<\/a>:<\/b><span style=\"font-weight: 400;\"> Automatically categorizing texts based on their content. For example, in content management systems, articles can be automatically classified into categories such as sports, politics, technology, etc.<\/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-96e50dc elementor-widget elementor-widget-image\" data-id=\"96e50dc\" 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=\"854\" height=\"450\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier3.jpg\" class=\"attachment-full size-full wp-image-188733\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier3.jpg 854w, https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier3-300x158.jpg 300w, https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier3-768x405.jpg 768w\" sizes=\"(max-width: 854px) 100vw, 854px\" \/>\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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3bcb234 e-flex e-con-boxed e-con\" data-id=\"3bcb234\" 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-bc7de93 elementor-widget elementor-widget-heading\" data-id=\"bc7de93\" 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 Advantages and Disadvantages?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7be074d elementor-widget elementor-widget-heading\" data-id=\"7be074d\" 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\">Advantages<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-afcaea8 elementor-widget elementor-widget-text-editor\" data-id=\"afcaea8\" 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<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Simplicity:<\/b><span style=\"font-weight: 400;\"> Easy to understand and implement. The Naive Bayes classifier is simple to code and does not require many tuning parameters.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Speed:<\/b><span style=\"font-weight: 400;\"> Computationally efficient, even with large datasets. Due to its simplicity, the Naive Bayes classifier is extremely fast to train and predict.<\/span><\/li><li><b>Performance:<\/b><span style=\"font-weight: 400;\"> Can be highly effective, especially with textual data. Despite its simplistic assumptions, it often provides competitive results compared to more complex models, particularly in text classification tasks.<\/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-59a29b3 elementor-widget elementor-widget-heading\" data-id=\"59a29b3\" 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\">Disadvantages<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae3feb7 elementor-widget elementor-widget-text-editor\" data-id=\"ae3feb7\" 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<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Independence Assumption:<\/b><span style=\"font-weight: 400;\"> The independence assumption among predictors is often unrealistic. In many practical cases, the features are not actually independent, which can lead to suboptimal predictions.<\/span><\/li><li><b>Variable Performance:<\/b><span style=\"font-weight: 400;\"> Can be outperformed by more sophisticated classification methods when the data does not meet the basic assumptions. In contexts where the relationships between features are complex, more advanced models like support vector machines or neural networks can offer better performance.<\/span><\/li><\/ul>\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-1323155 e-flex e-con-boxed e-con\" data-id=\"1323155\" 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-4fa7b9e elementor-widget elementor-widget-heading\" data-id=\"4fa7b9e\" 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-f20abc2 elementor-widget elementor-widget-text-editor\" data-id=\"f20abc2\" 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;\">The Naive Bayes classifier remains <a href=\"https:\/\/datascientest.com\/en\/google-colab-the-power-of-the-cloud-for-machine-learning\">a valuable tool in machine learning<\/a> due to its simplicity and effectiveness. Although it relies on simplified assumptions, it offers <\/span><b>remarkable performance<\/b><span style=\"font-weight: 400;\"> for a <\/span><b>wide range of applications<\/b><span style=\"font-weight: 400;\">. Whether for spam filtering, sentiment analysis, or document classification, Naive Bayes is often an effective first approach to consider for supervised classification.<\/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-6933108 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"6933108\" 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\">Training in classification algorithms<\/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-6b5d0e8 e-flex e-con-boxed e-con\" data-id=\"6b5d0e8\" 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-a8b3b56 elementor-widget elementor-widget-html\" data-id=\"a8b3b56\" 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\": \"What is the Naive Bayes classifier?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"The Naive Bayes classifier is a classification method based on Bayes' theorem. It is considered 'naive' because it assumes that the features of the data are independent of each other, which is rarely the case in reality. Despite this simplifying assumption, the Naive Bayes classifier often performs well and is efficient for classification tasks, especially with large datasets.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the most common types of Naive Bayes classifiers?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"There are several variants of the Naive Bayes classifier tailored to different types of data: \\n - Gaussian Naive Bayes \\n - Multinomial Naive Bayes \\n - Bernoulli Naive Bayes\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the advantages of the Naive Bayes classifier?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"\\n - Easy to understand and implement. \\n - Very fast to train and predict, even with large amounts of data. \\n - Works well with small training datasets.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the disadvantages of the Naive Bayes classifier?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"\\n - The assumption that features are independent is often unrealistic, which can affect performance. \\n - Can be biased if the classes are highly imbalanced. \\n - Less effective than more sophisticated methods on complex datasets where relationships between features are important.\"\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>The Naive Bayes classifier is a classification technique based on Bayes&#8217; theorem, with the na\u00efve assumption of independence among predictors. Despite its simplicity, the Naive Bayes classifier has demonstrated its effectiveness in various application areas, including spam filtering, sentiment analysis, and document classification. What Is the Theory Behind the Naive Bayes Classifier? The Naive Bayes [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":188706,"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>Naive Bayes Classifier: Theory and Application<\/title>\n<meta name=\"description\" content=\"Discover Naive Bayes classifier, a powerful and simple-to-implement machine learning algorithm, ideal for classification tasks. Learn its theory, applications, and how to use it effectively.\" \/>\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\/all-about-naive-bayes-classifier\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Naive Bayes Classifier: Theory and Application\" \/>\n<meta property=\"og:description\" content=\"The Naive Bayes classifier is a classification technique based on Bayes&#039; theorem, with the na\u00efve assumption of independence among predictors. Despite its\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2024-09-13T05:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-09-06T09:54:01+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier-DataScientest.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier\"},\"author\":{\"name\":\"Daniel\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0\"},\"headline\":\"Naive Bayes Classifier: Theory and Application\",\"datePublished\":\"2024-09-13T05:30:00+00:00\",\"dateModified\":\"2024-09-06T09:54:01+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier\"},\"wordCount\":1124,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"articleSection\":[\"Data Science\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier#respond\"]}],\"copyrightYear\":\"2024\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier\",\"url\":\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier\",\"name\":\"Naive Bayes Classifier: Theory and Application\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/#website\"},\"datePublished\":\"2024-09-13T05:30:00+00:00\",\"dateModified\":\"2024-09-06T09:54:01+00:00\",\"description\":\"Discover Naive Bayes classifier, a powerful and simple-to-implement machine learning algorithm, ideal for classification tasks. Learn its theory, applications, and how to use it effectively.\",\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/en\/#website\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"name\":\"Data Science Courses | DataScientest\",\"description\":\"Leader in Data Science Courses\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/en\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/en\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0\",\"name\":\"Daniel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g\",\"caption\":\"Daniel\"},\"sameAs\":[\"https:\/\/www.linkedin.com\/school\/datascientest\/\",\"user\"],\"jobTitle\":\"ing\u00e9nieur logiciel\",\"worksFor\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/author\/team-redac\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Naive Bayes Classifier: Theory and Application","description":"Discover Naive Bayes classifier, a powerful and simple-to-implement machine learning algorithm, ideal for classification tasks. Learn its theory, applications, and how to use it effectively.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier","og_locale":"en_GB","og_type":"article","og_title":"Naive Bayes Classifier: Theory and Application","og_description":"The Naive Bayes classifier is a classification technique based on Bayes' theorem, with the na\u00efve assumption of independence among predictors. Despite its","og_url":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier","og_site_name":"Data Science Courses | DataScientest","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2024-09-13T05:30:00+00:00","article_modified_time":"2024-09-06T09:54:01+00:00","og_image":[{"width":1200,"height":600,"url":"https:\/\/datascientest.com\/en\/files\/2024\/08\/Naive-Bayes-Classifier-DataScientest.png","type":"image\/png"}],"author":"Daniel","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Written by":"Daniel","Estimated reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier#article","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier"},"author":{"name":"Daniel","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0"},"headline":"Naive Bayes Classifier: Theory and Application","datePublished":"2024-09-13T05:30:00+00:00","dateModified":"2024-09-06T09:54:01+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier"},"wordCount":1124,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"articleSection":["Data Science"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier#respond"]}],"copyrightYear":"2024","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier","url":"https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier","name":"Naive Bayes Classifier: Theory and Application","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/#website"},"datePublished":"2024-09-13T05:30:00+00:00","dateModified":"2024-09-06T09:54:01+00:00","description":"Discover Naive Bayes classifier, a powerful and simple-to-implement machine learning algorithm, ideal for classification tasks. Learn its theory, applications, and how to use it effectively.","inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/en\/all-about-naive-bayes-classifier"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/en\/#website","url":"https:\/\/datascientest.com\/en\/","name":"Data Science Courses | DataScientest","description":"Leader in Data Science Courses","publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/en\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0","name":"Daniel","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g","caption":"Daniel"},"sameAs":["https:\/\/www.linkedin.com\/school\/datascientest\/","user"],"jobTitle":"ing\u00e9nieur logiciel","worksFor":"DataScientest","url":"https:\/\/datascientest.com\/en\/author\/team-redac"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/188704"}],"collection":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/users\/45"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/comments?post=188704"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/188704\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media\/188706"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media?parent=188704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/categories?post=188704"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/tags?post=188704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}