{"id":16565,"date":"2024-12-05T09:49:18","date_gmt":"2024-12-05T09:49:18","guid":{"rendered":"https:\/\/www.pickl.ai\/blog\/?p=16565"},"modified":"2024-12-05T09:49:19","modified_gmt":"2024-12-05T09:49:19","slug":"naive-bayes-types-examples","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/","title":{"rendered":"Naive Bayes Uncovered: Types, Examples, and Real-World Applications"},"content":{"rendered":"\n<p><strong>Summary:<\/strong> Naive Bayes classifiers are a family of probabilistic models based on Bayes&#8217; theorem, widely used for classification tasks. They assume that features are conditionally independent given the class label, which simplifies computations. Common applications include text classification, spam detection, and sentiment analysis due to their speed and effectiveness with large datasets.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#What_is_Naive_Bayes\" >What is Naive Bayes?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#How_Does_Naive_Bayes_Work\" >How Does Naive Bayes Work?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#The_Naive_Assumption\" >The Naive Assumption<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Steps_in_Naive_Bayes_Classification\" >Steps in Naive Bayes Classification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Example_Email_Spam_Classification\" >Example: Email Spam Classification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Types_of_Naive_Bayes_Classifiers\" >Types of Naive Bayes Classifiers<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Multinomial_Naive_Bayes_Classifier\" >Multinomial Naive Bayes Classifier<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Example\" >Example<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Bernoulli_Naive_Bayes_Classifier\" >Bernoulli Naive Bayes Classifier<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Example-2\" >Example<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Gaussian_Naive_Bayes_Classifier\" >Gaussian Naive Bayes Classifier<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Example-3\" >Example<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#What_is_the_Main_Principle_Behind_Naive_Bayes\" >What is the Main Principle Behind Naive Bayes?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#In_What_Scenarios_Is_Naive_Bayes_Most_Effective\" >In What Scenarios Is Naive Bayes Most Effective?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/naive-bayes-types-examples\/#What_are_Some_Limitations_of_Using_Naive_Bayes\" >What are Some Limitations of Using Naive Bayes?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><strong>Introduction<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><a href=\"https:\/\/pickl.ai\/blog\/10-machine-learning-algorithms-you-need-to-know-in-2024\/\">Naive Bayes<\/a> is a powerful and widely used classification algorithm in <a href=\"https:\/\/pickl.ai\/blog\/types-of-machine-learning\/\">Machine Learning<\/a>, particularly known for its simplicity and effectiveness. It operates on the principle of Bayes&#8217; theorem, which relates the conditional and marginal probabilities of random events.<\/p>\n\n\n\n<p>Naive Bayes classifiers are employed in numerous practical scenarios. For instance, email services use to filter spam messages by analyzing the frequency of certain words. If an email contains terms like &#8220;free,&#8221; &#8220;winner,&#8221; or &#8220;click here,&#8221; it might be classified as spam based on learned probabilities.<\/p>\n\n\n\n<p>Another example is in sentiment analysis, It can determine whether a customer review is positive or negative by evaluating the presence of specific words or phrases.<\/p>\n\n\n\n<p>In healthcare, It can predict whether a patient has a particular disease based on symptoms and medical history. For example, it can analyze symptoms like fever, cough, and fatigue to classify whether a patient might have the flu.<\/p>\n\n\n\n<p>These examples highlight how it provides quick and efficient solutions across various domains.<\/p>\n\n\n\n<p><strong>Key Takeaways<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Naive Bayes classifiers are based on Bayes&#8217; theorem for efficient classification.<\/li>\n\n\n\n<li>They assume feature independence, simplifying calculations in models.<\/li>\n\n\n\n<li>Common applications include text classification and spam filtering.<\/li>\n\n\n\n<li>Despite assumptions, they perform well in many real-world scenarios.<\/li>\n\n\n\n<li>Naive Bayes is fast, making it suitable for large datasets.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"what-is-naive-bayes\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_Naive_Bayes\"><\/span><strong>What is Naive Bayes?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Naive Bayes is a family of probabilistic algorithms based on applying Bayes&#8217; theorem with strong (naive) independence assumptions between the features. In simpler terms, it assumes that the presence of a particular feature in a class is independent of other features.<\/p>\n\n\n\n<p>Despite this assumption being unrealistic in many real-world situations, It often performs surprisingly well.<\/p>\n\n\n\n<p>The algorithm is particularly useful for large datasets because it requires only a small amount of training data to estimate the parameters necessary for classification.<\/p>\n\n\n\n<h2 id=\"how-does-naive-bayes-work\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_Does_Naive_Bayes_Work\"><\/span><strong>How Does Naive Bayes Work?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>At the core of Naive Bayes is <a href=\"https:\/\/pickl.ai\/blog\/10-machine-learning-algorithms-you-need-to-know-in-2024\/\"><strong>Bayes&#8217; theorem<\/strong><\/a>, which describes the probability of an event based on prior knowledge of conditions related to the event. The formula for Bayes&#8217; theorem is:<\/p>\n\n\n\n<p>P(Y\u2223X)=P(X\u2223Y)\u22c5P(Y)P(X)<br><br><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdIPVPNLBBsKezxFJZGJRF8zYgazdJPno-6OUpcdmRG_Xnic3aVBEbCTyLKxrNCkRnHG44dTEToJsRrTSfUhK4pz_4lAZUmmn9Ft8lJCsIYOxGYAfPK7FpPLkNftmISLkFZC6o68Q?key=_qE4xCqmBCdQtmyOlCabppVA\" width=\"282\" height=\"83\"><\/p>\n\n\n\n<p>Where:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>P(Y\u2223X)<em>P<\/em>(<em>Y<\/em>\u2223<em>X<\/em>) is the <strong>posterior probability<\/strong>: the probability of class Y<em>Y<\/em> given the feature X<em>X<\/em>.<\/li>\n\n\n\n<li>P(X\u2223Y)<em>P<\/em>(<em>X<\/em>\u2223<em>Y<\/em>) is the <strong>likelihood<\/strong>: the probability of feature X<em>X<\/em> given class Y<em>Y<\/em>.<\/li>\n\n\n\n<li>P(Y)<em>P<\/em>(<em>Y<\/em>) is the <strong>prior probability<\/strong>: the initial probability of class Y<em>Y<\/em>.<\/li>\n\n\n\n<li>P(X)<em>P<\/em>(<em>X<\/em>) is the <strong>evidence<\/strong>: the total probability of feature X<em>X<\/em>.<\/li>\n<\/ul>\n\n\n\n<p>In practice, calculating P(X\u2223Y)<em>P<\/em>(<em>X<\/em>\u2223<em>Y<\/em>) and P(Y)<em>P<\/em>(<em>Y<\/em>) from training data allows us to predict classes for new instances.<\/p>\n\n\n\n<h3 id=\"the-naive-assumption\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Naive_Assumption\"><\/span><strong>The Naive Assumption<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The &#8220;naive&#8221; aspect of Naive Bayes comes from its assumption that all features are independent given the class label. This means that knowing one feature does not provide any information about another feature.&nbsp;<\/p>\n\n\n\n<p>For example, if we are predicting whether an email is spam based on words present in it, the algorithm assumes that each word&#8217;s presence contributes independently to the probability of it being spam.<\/p>\n\n\n\n<h2 id=\"steps-in-naive-bayes-classification\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Steps_in_Naive_Bayes_Classification\"><\/span><strong>Steps in Naive Bayes Classification<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>classification is a straightforward yet powerful technique based on Bayes&#8217; Theorem. It is particularly effective for large datasets and is widely used in applications like text classification. Here are the key steps involved in the Naive Bayes classification process:<\/p>\n\n\n\n<p><strong>Step 1: Training Phase<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Collect training data with features and corresponding class labels.<\/li>\n\n\n\n<li>Calculate prior probabilities for each class based on their frequency in the dataset.<\/li>\n\n\n\n<li>For each feature, calculate likelihood probabilities (conditional probabilities) based on their<\/li>\n\n\n\n<li>occurrences within each class.<\/li>\n<\/ul>\n\n\n\n<p><strong>Step 2: Prediction Phase<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>For a new instance to classify, calculate posterior probabilities for each class using Bayes&#8217; theorem.<\/li>\n\n\n\n<li>Since P(X)<em>P<\/em>(<em>X<\/em>) remains constant across classes during classification, it can be ignored when comparing probabilities.<\/li>\n\n\n\n<li>The class with the highest posterior probability is selected as the predicted class.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"example-email-spam-classification\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example_Email_Spam_Classification\"><\/span><strong>Example: Email Spam Classification<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To illustrate how Naive Bayes works, consider a simple example where we classify emails as either &#8220;Spam&#8221; or &#8220;Not Spam.&#8221; Suppose we have the following training data:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Spam emails contain words like &#8220;free,&#8221; &#8220;win,&#8221; and &#8220;money.&#8221;<\/li>\n\n\n\n<li>Not Spam emails contain words like &#8220;meeting,&#8221; &#8220;schedule,&#8221; and &#8220;project.&#8221;<\/li>\n<\/ul>\n\n\n\n<p><strong>Calculate Prior Probabilities<\/strong><\/p>\n\n\n\n<p>If out of 100 emails, 40 are spam and 60 are not spam:&nbsp;<\/p>\n\n\n\n<p>P(Not\\Spam)=\\frac{60}{100}=0.6<\/p>\n\n\n\n<p><strong>Calculate Likelihoods<\/strong><\/p>\n\n\n\n<p>If &#8220;free&#8221; appears in 30 spam emails and 5 not spam emails:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>P(free|Spam)=\\frac{30}{40}=0.75<\/li>\n\n\n\n<li>P(free|Not\\Spam)=\\frac{5}{60}=0.083<\/li>\n<\/ul>\n\n\n\n<p><strong>Make Predictions<\/strong><\/p>\n\n\n\n<p>For an incoming email containing the word &#8220;free&#8221;<\/p>\n\n\n\n<p>Calculate posterior probabilities:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>P(Spam|free)\\propto P(free|Spam)\\cdot P(Spam)=0.75\\cdot 0.4=0.3<\/li>\n\n\n\n<li>P(Not\\Spam|free)\\propto P(free|Not\\Spam)\\cdot P(Not\\Spam)=0.083\\cdot 0.6=0.05<\/li>\n\n\n\n<li>Since P(Spam|free)>P(Not\\Spam|free), the email is classified as Spam.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"types-of-naive-bayes-classifiers\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Types_of_Naive_Bayes_Classifiers\"><\/span><strong>Types of Naive Bayes Classifiers<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Naive Bayes classifiers are a family of probabilistic classifiers based on Bayes&#8217; theorem, which assumes that the presence of a particular feature in a class is independent of the presence of any other feature.&nbsp;<\/p>\n\n\n\n<p>This assumption simplifies the computation and makes the algorithm efficient for classification tasks. There are three primary types of classifiers, each suited for different types of data:<\/p>\n\n\n\n<h3 id=\"multinomial-naive-bayes-classifier\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Multinomial_Naive_Bayes_Classifier\"><\/span><strong>Multinomial Naive Bayes Classifier<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This classifier is used primarily for document classification tasks where the features represent the frequencies of words in a document. It assumes that the features follow a multinomial distribution.<\/p>\n\n\n\n<h4 id=\"example\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example\"><\/span><strong>Example<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>A common application is in text classification, such as categorizing emails as &#8220;spam&#8221; or &#8220;not spam.&#8221; In this case, the features could be the counts of each word in the email.<\/p>\n\n\n\n<h3 id=\"bernoulli-naive-bayes-classifier\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Bernoulli_Naive_Bayes_Classifier\"><\/span><strong>Bernoulli Naive Bayes Classifier<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The Bernoulli Naive Bayes classifier is similar to the multinomial model but works with binary\/boolean features. It assumes that each feature is independent and indicates whether a word occurs in a document (1) or not (0).<\/p>\n\n\n\n<h4 id=\"example-2\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example-2\"><\/span><strong>Example<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>This model is also used in text classification, particularly when dealing with binary occurrences of terms, such as determining if an email contains specific keywords that classify it as spam.<\/p>\n\n\n\n<h3 id=\"gaussian-naive-bayes-classifier\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Gaussian_Naive_Bayes_Classifier\"><\/span><strong>Gaussian Naive Bayes Classifier<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This classifier is used when the features are continuous and assumes that these features follow a Gaussian (normal) distribution. It calculates probabilities based on the mean and variance of the features.<\/p>\n\n\n\n<h4 id=\"example-3\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example-3\"><\/span><strong>Example<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>An application could be in medical diagnosis, where attributes like blood pressure or cholesterol levels are continuous variables used to classify patients into categories such as &#8220;healthy&#8221; or &#8220;at risk&#8221; based on their measurements<\/p>\n\n\n\n<h2 id=\"conclusion\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Naive Bayes classifiers provide an effective means for solving classification problems across various domains due to their simplicity and efficiency in handling large datasets. While they operate under strong independence assumptions that may not always hold true in practice, they frequently deliver accurate results in applications such as spam filtering, sentiment analysis, and medical diagnosis.<\/p>\n\n\n\n<h2 id=\"frequently-asked-questions\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><strong>Frequently Asked Questions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 id=\"what-is-the-main-principle-behind-naive-bayes\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_the_Main_Principle_Behind_Naive_Bayes\"><\/span><strong>What is the Main Principle Behind Naive Bayes?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Naive Bayes operates on Bayes&#8217; theorem and assumes that all features are independent given the class label, allowing for efficient probability calculations for classification tasks.<\/p>\n\n\n\n<h3 id=\"in-what-scenarios-is-naive-bayes-most-effective\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"In_What_Scenarios_Is_Naive_Bayes_Most_Effective\"><\/span><strong>In What Scenarios Is Naive Bayes Most Effective?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Naive Bayes excels in text classification tasks such as spam detection and sentiment analysis due to its ability to handle high-dimensional data efficiently while providing quick predictions.<\/p>\n\n\n\n<h3 id=\"what-are-some-limitations-of-using-naive-bayes\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_are_Some_Limitations_of_Using_Naive_Bayes\"><\/span><strong>What are Some Limitations of Using Naive Bayes?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The primary limitations include its assumption of feature independence and potential inaccuracies when encountering unseen feature values during prediction unless techniques like Laplace 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