{"id":25534831,"date":"2022-07-24T18:12:39","date_gmt":"2022-07-24T12:42:39","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25534831"},"modified":"2023-03-10T14:37:00","modified_gmt":"2023-03-10T09:07:00","slug":"introduction-to-classification-algorithms-in-machine-learning","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/introduction-to-classification-algorithms-in-machine-learning\/","title":{"rendered":"Introduction to Classification Algorithms in Machine Learning"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e76d7d5b44e\" class=\"ez-toc-cssicon-toggle-label\"><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><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e76d7d5b44e\"  aria-label=\"Toggle\" \/><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:\/\/entri.app\/blog\/introduction-to-classification-algorithms-in-machine-learning\/#What_Is_Classification\" >What Is Classification?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/introduction-to-classification-algorithms-in-machine-learning\/#Top_5_Classification_Algorithms_in_Machine_Learning\" >Top 5 Classification Algorithms in Machine Learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/introduction-to-classification-algorithms-in-machine-learning\/#4_Applications_of_Classification_Algorithms\" >4 Applications of Classification Algorithms<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/entri.app\/blog\/introduction-to-classification-algorithms-in-machine-learning\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<p>Classification is a natural language processing task that depends on\u00a0machine learning algorithms.<\/p>\n<p>There are many different types of classification tasks that you can perform, the most popular being\u00a0sentiment analysis. Each task often requires a different algorithm because each one is used to solve a specific problem.<\/p>\n<p>Computer Scientist David Wolpert explains in his paper, The Lack of A Priori Distinctions Between Learning Algorithms. Neural Computation, that:<\/p>\n<p><em>&#8220;For each problem, you must select the right algorithm. Your question is how to do this. If you have plenty of computational resources, you can test multiple algorithms and parameter settings. In this approach, the main question is how to estimate and compare the performance of the algorithms in a reliable way.&#8221;<\/em><\/p>\n<p>Before introducing you to the different types of classification algorithms to choose from, let\u2019s quickly go over what classification is.<\/p>\n<p><a href=\"https:\/\/bit.ly\/3ELmCiA\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25520910 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png\" alt=\"Python and Machine Learning Square\" width=\"345\" height=\"345\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png 345w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-300x300.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-150x150.png 150w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-24x24.png 24w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-48x48.png 48w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-96x96.png 96w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-75x75.png 75w\" sizes=\"auto, (max-width: 345px) 100vw, 345px\" \/><\/a><\/p>\n<h2 id=\"what-is\" class=\"section\"><span class=\"ez-toc-section\" id=\"What_Is_Classification\"><\/span><strong>What Is Classification?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Classification\u00a0is the process of recognizing, understanding, and grouping ideas and objects into preset categories or \u201csub-populations.\u201d Using pre-categorized training datasets, machine learning programs use a variety of algorithms to classify future datasets into categories.<\/p>\n<p>Classification algorithms in\u00a0machine learning\u00a0use input training data to predict the likelihood that subsequent data will fall into one of the predetermined categories. One of the most common uses of classification is filtering emails into \u201cspam\u201d or \u201cnon-spam.\u201d<\/p>\n<p>In short, classification is a form of \u201cpattern recognition,\u201d with classification algorithms applied to the training data to find the same pattern (similar words or sentiments, number sequences, etc.) in future sets of data.<\/p>\n<p>Using classification algorithms, which we will go into more detail about below, text analysis software can perform tasks like aspect-based sentiment analysis to categorize unstructured text by topic and polarity of opinion (positive, negative, neutral, and beyond).<\/p>\n<p>Try out this pre-trained\u00a0sentiment classifier\u00a0to understand how classification algorithms work in practice, then read on to learn more about different types of classification algorithms.<\/p>\n<h2 id=\"algorithms\" class=\"section\"><span class=\"ez-toc-section\" id=\"Top_5_Classification_Algorithms_in_Machine_Learning\"><\/span><strong>Top 5 Classification Algorithms in Machine Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The study of classification in statistics is vast, and there are several types of classification algorithms you can use depending on the dataset you are working with. Below are five of the most common algorithms in machine learning.<\/p>\n<h3><strong>Popular Classification Algorithms:<\/strong><\/h3>\n<ul>\n<li>Logistic Regression<\/li>\n<li>Naive Bayes<\/li>\n<li>K-Nearest Neighbors<\/li>\n<li>Decision Tree<\/li>\n<li>Support Vector Machines<\/li>\n<\/ul>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">Get a free demo for\u00a0 our certificate program in data science and Machine learning<\/a><\/h4>\n<h3 id=\"logistic-regression\" class=\"section\"><strong>Logistic Regression<\/strong><\/h3>\n<p>Logistic regression is a calculation used to predict a binary outcome: either something happens, or does not. This can be exhibited as Yes\/No, Pass\/Fail, Alive\/Dead, etc.<\/p>\n<p>Independent variables are analyzed to determine the binary outcome with the results falling into one of two categories. The independent variables can be categorical or numeric, but the dependent variable is always categorical. Written like this:<\/p>\n<p><strong><em>P(Y=1|X) or P(Y=0|X)<\/em><\/strong><\/p>\n<p>It calculates the probability of dependent variable\u00a0<em>Y<\/em>, given independent variable\u00a0<em>X<\/em>.<\/p>\n<p>This can be used to calculate the probability of a word having a positive or negative connotation (0, 1, or on a scale between). Or it can be used to determine the object contained in a photo (tree, flower, grass, etc.), with each object given a probability between 0 and 1.<\/p>\n<h3 id=\"naive-bayes\" class=\"section\"><strong>Naive Bayes<\/strong><\/h3>\n<p>Naive Bayes\u00a0calculates the possibility of whether a data point belongs within a certain category or does not. In\u00a0text analysis, it can be used to categorize words or phrases as belonging to a preset \u201ctag\u201d (classification) or not. For example:<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAALCAIAAADwazoUAAAACXBIWXMAAAsTAAALEwEAmpwYAAABCklEQVQoz3WS2XaDMAxE8\/8\/2iYgy6s2DOpxOE0KJfMoW74zkm+1NsC0\/mq7krunXGMq7v4qrut6q41miP7U9kHunksFHNdegNEsoiGmB+D3BI3YP2gwQlS1V2XbtpuoNiJmacS9d\/ft0ryIEHPv68E2MWPME4SACVOeAVPKpwjuXmoLmB4zEMt+OprNFn6+SizDAcslWdWIWdUOmZkFQgxxYAMm4uvYpbYZcFmWQ2YzE1ERVbMnePCJRVX\/klmEWXrvh8yiChhH0lLvE0wQJsCv+5xzPU17ArQTeUy7sYjWRo241Mai\/23v68ilmtm7Wc1iKrmMD5RKxZhbo9Nvc3cijrlgzPuqd9s\/Wj+EAFnmjRgAAAAASUVORK5CYII=\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/078adf261ec6272ef8ec9434f2fd25d3829c4115\/58820\/static\/2272681807936c3b76f89e3c257e4fd9\/8c680\/table.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/01985860edf5b4516a41c6cfced5548ba98388fa\/ea410\/static\/2272681807936c3b76f89e3c257e4fd9\/35ce1\/table.png 750w,https:\/\/d33wubrfki0l68.cloudfront.net\/aaf22f323f0aa3ee917c35a29a780f4550634d33\/1fdf4\/static\/2272681807936c3b76f89e3c257e4fd9\/85796\/table.png 1080w,https:\/\/d33wubrfki0l68.cloudfront.net\/078adf261ec6272ef8ec9434f2fd25d3829c4115\/58820\/static\/2272681807936c3b76f89e3c257e4fd9\/8c680\/table.png 1246w\" alt=\"\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<p>To decide whether or not a phrase should be tagged as \u201csports,\u201d you need to calculate:<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAADCAYAAACTWi8uAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAaElEQVQI142OvQrAIAyEff9XdFBwUAdR8AcU9aShliIdenBDLrmPMNyac+JLpRSEEJBzxhgDKSXy7mzvmZ3AGCOstZBSUtE5B6UUOOcQQsAYQ\/vfwOuL1hpqrei9P7n3Hlpryt73J3ABb5bqwZ70KboAAAAASUVORK5CYII=\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/4505b4951a21e5a1f7eabd351994f74720e90998\/bb7c4\/static\/9d54d63a43976955f315419a1d8efb48\/b2e39\/formula.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/9ec24e1acbd51dec44c214f1c7518df60aa81658\/e75bd\/static\/9d54d63a43976955f315419a1d8efb48\/ca5b2\/formula.png 750w,https:\/\/d33wubrfki0l68.cloudfront.net\/a55cafc9a8405930e990d109f1987a6de9924d45\/8d8ea\/static\/9d54d63a43976955f315419a1d8efb48\/16e8f\/formula.png 1080w,https:\/\/d33wubrfki0l68.cloudfront.net\/57cd74729bfff5c5ce8aa387a0a68bb064206c9b\/de5d6\/static\/9d54d63a43976955f315419a1d8efb48\/e0345\/formula.png 1366w,https:\/\/d33wubrfki0l68.cloudfront.net\/4505b4951a21e5a1f7eabd351994f74720e90998\/bb7c4\/static\/9d54d63a43976955f315419a1d8efb48\/b2e39\/formula.png 1374w\" alt=\"Naive Bayes formula.\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<p><em>Or\u2026 the probability of A, if B is true, is equal to the probability of B, if A is true, times the probability of A being true, divided by the probability of B being true<\/em>.<\/p>\n<h3 id=\"knn\" class=\"section\"><strong>K-nearest Neighbors<\/strong><\/h3>\n<p>K-nearest neighbors (k-NN) is a pattern recognition algorithm that uses training datasets to find the\u00a0<em>k<\/em>\u00a0closest relatives in future examples.<\/p>\n<p>When k-NN is used in classification, you calculate to place data within the category of its nearest neighbor. If\u00a0<em>k<\/em>\u00a0= 1, then it would be placed in the class nearest 1.\u00a0<em>K<\/em> is classified by a plurality poll of its neighbors.<\/p>\n<h3 id=\"decision-tree\" class=\"section\"><strong>Decision Tree<\/strong><\/h3>\n<p>A decision tree is a supervised learning algorithm that is perfect for classification problems, as it\u2019s able to order classes on a precise level. It works like a flow chart, separating data points into two similar categories at a time from the \u201ctree trunk\u201d to \u201cbranches,\u201d to \u201cleaves,\u201d where the categories become more finitely similar. This creates categories within categories, allowing for organic classification with limited human supervision.<\/p>\n<h4 style=\"text-align: center;\"><\/h4>\n<p>To continue with the sports example, this is how the decision tree works:<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,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\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/bddb96f39a827c4179d948973373a894f0051fbe\/25f79\/static\/b42ef5448b11ec2f2ec20ca7f97cbb3c\/05f56\/decision-tree-sports.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/20cc32dc2fa5d34b67d283c4012802f9036ad825\/9f7dd\/static\/b42ef5448b11ec2f2ec20ca7f97cbb3c\/cf7f0\/decision-tree-sports.png 750w,https:\/\/d33wubrfki0l68.cloudfront.net\/bddb96f39a827c4179d948973373a894f0051fbe\/25f79\/static\/b42ef5448b11ec2f2ec20ca7f97cbb3c\/05f56\/decision-tree-sports.png 1026w\" alt=\"An example of a decision tree dividing different sports.\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<h4 id=\"random-forest\" class=\"section\"><strong>Random Forest<\/strong><\/h4>\n<p>The random forest algorithm is an expansion of decision tree, in that you first construct a multitude of decision trees with training data, then fit your new data within one of the trees as a \u201crandom forest.\u201d<\/p>\n<p>It, essentially, averages your data to connect it to the nearest tree on the data scale. Random forest models are helpful as they remedy for the decision tree\u2019s problem of \u201cforcing\u201d data points within a category unnecessarily.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">Learn data science and machine learning course<\/a><\/h4>\n<h3 id=\"svm\" class=\"section\"><strong>Support Vector Machines<\/strong><\/h3>\n<p>A\u00a0support vector machine (SVM)\u00a0uses algorithms to train and classify data within degrees of polarity, taking it to a degree beyond\u00a0<em>X\/Y<\/em>\u00a0prediction.<\/p>\n<p>For a simple visual explanation, we will use two tags: <em>red<\/em>\u00a0and\u00a0<em>blue<\/em>, with two data features:\u00a0<em>X<\/em>\u00a0and\u00a0<em>Y<\/em>, then train our classifier to output an<em>\u00a0X\/Y<\/em>\u00a0coordinate as either\u00a0<em>red<\/em>\u00a0or\u00a0<em>blue<\/em>.<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,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\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/80fcd28fcaabbd3209626dff7c00515a2d8bbe79\/69374\/static\/93a102a9b7b96d9047212e15b627724b\/ae702\/svm-example-1.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/80fcd28fcaabbd3209626dff7c00515a2d8bbe79\/69374\/static\/93a102a9b7b96d9047212e15b627724b\/ae702\/svm-example-1.png 560w\" alt=\"\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<p>The SVM then assigns a hyperplane that best separates the tags. In two dimensions this is simply a line. Anything on one side of the line is\u00a0<em>red<\/em>\u00a0and anything on the other side is\u00a0<em>blue<\/em>. In sentiment analysis, for example, this would be\u00a0<em>positive<\/em>\u00a0and\u00a0<em>negative<\/em>.<\/p>\n<p>In order to maximize machine learning, the best hyperplane is the one with the largest distance between each tag:<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,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\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/513edcee1cdb0fb3220736a6e226ed34c4df946c\/928a1\/static\/e662f65502ffd24d3ee23c07efe88d9e\/ae702\/svm-example-2.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/513edcee1cdb0fb3220736a6e226ed34c4df946c\/928a1\/static\/e662f65502ffd24d3ee23c07efe88d9e\/ae702\/svm-example-2.png 560w\" alt=\"\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<p>However, as data sets become more complex, it may not be possible to draw a single line to classify the data into two camps:<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,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\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/514f7b8e9a55bb28631e0d8321399472276f545f\/84e83\/static\/5db2d9178789315ce9fa42f579c895a6\/95f64\/svm-example-3.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/514f7b8e9a55bb28631e0d8321399472276f545f\/84e83\/static\/5db2d9178789315ce9fa42f579c895a6\/95f64\/svm-example-3.png 657w\" alt=\"\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<p>Using SVM, the more complex the data, the more accurate the predictor will become. Imagine the above in three dimensions, with a\u00a0<em>Z-axis<\/em>\u00a0added, so it becomes a circle.<\/p>\n<p>Mapped back to two dimensions with the best hyperplane, it looks like this:<\/p>\n<div>\n<div class=\"gatsby-image-wrapper\" data-gatsby-image-wrapper=\"\">\n<div aria-hidden=\"true\"><\/div>\n<p><img decoding=\"async\" src=\"image\/png;base64,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\" alt=\"\" aria-hidden=\"true\" data-placeholder-image=\"\" \/><img decoding=\"async\" src=\"https:\/\/d33wubrfki0l68.cloudfront.net\/a6208aad54f275a296e3a28bdd6e3763d676be3c\/48324\/static\/583405ebadf21c9691030ec4bb875e48\/95f64\/svm-example-4.png\" srcset=\"https:\/\/d33wubrfki0l68.cloudfront.net\/a6208aad54f275a296e3a28bdd6e3763d676be3c\/48324\/static\/583405ebadf21c9691030ec4bb875e48\/95f64\/svm-example-4.png 657w\" alt=\"\" data-gatsby-image-ssr=\"\" data-main-image=\"\" \/><\/p>\n<\/div>\n<\/div>\n<p>SVM allows for more accurate machine learning because it\u2019s multidimensional.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\">Join for data science and machine learning course in Entri app<\/a><span style=\"color: #ff0000;\">! Get a free demo<\/span><\/h4>\n<h2 id=\"applications\" class=\"section\"><span class=\"ez-toc-section\" id=\"4_Applications_of_Classification_Algorithms\"><\/span><strong>4 Applications of Classification Algorithms<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Okay, so now we understand a bit of the mathematics behind classification, but what can these machine learning algorithms do with real-world data?<\/p>\n<ul>\n<li>Sentiment Analysis<\/li>\n<li>Email Spam Classification<\/li>\n<li>Document Classification<\/li>\n<li>Image Classification<\/li>\n<\/ul>\n<h3 id=\"sentiment\" class=\"section\"><strong>Sentiment Analysis<\/strong><\/h3>\n<p>Sentiment analysis\u00a0is a machine learning text analysis technique that assigns sentiment (opinion, feeling, or emotion) to words within a text, or an entire text, on a polarity scale of\u00a0<em>Positive<\/em>,\u00a0<em>Negative<\/em>, or\u00a0<em>Neutral<\/em>.<\/p>\n<p>It can automatically read through thousands of pages in minutes or constantly monitor social media for posts about you. The tweet below, for example, about the messaging app,\u00a0Slack, would be analyzed to pull all of the individual statements as\u00a0<em>Positive<\/em>. This allows companies to follow product releases and marketing campaigns in real-time, to see how customers are reacting.<\/p>\n<p>Using advanced machine learning algorithms, sentiment analysis models can be trained to read for things like sarcasm and misused or misspelled words. Once properly trained, models produce consistently accurate results in a fraction of the time it would take humans.<\/p>\n<h3 id=\"spam\" class=\"section\"><strong>Email Spam Classification<\/strong><\/h3>\n<p>One of the most common uses of classification, working non-stop and with little need for human interaction, email spam classification saves us from tedious deletion tasks and sometimes even costly phishing scams.<\/p>\n<p>Email applications use the above algorithms to calculate the likelihood that an email is either not intended for the recipient or unwanted spam. Using text analysis classification techniques, spam emails are weeded out from the regular inbox: perhaps a recipient\u2019s name is spelled incorrectly, or certain scamming keywords are used.<\/p>\n<p>Spam classifiers do still need to be trained to a degree, as we have all experienced when signing up for an email list of some sort that ends up in the spam folder.<\/p>\n<h3 id=\"document\" class=\"section\"><strong>Document Classification<\/strong><\/h3>\n<p>Document classification is the ordering of documents into categories according to their content. This was previously done manually, as in the library sciences or hand-ordered legal files. Machine learning classification algorithms, however, allow this to be performed automatically.<\/p>\n<p>Document classification differs from text classification, in that, entire documents, rather than just words or phrases, are classified. This is put into practice when using search engines online, cross-referencing topics in legal documents, and searching healthcare records by drug and diagnosis.<\/p>\n<h3 id=\"image\" class=\"section\"><strong>Image Classification<\/strong><\/h3>\n<p>Image classification assigns previously trained categories to a given image. These could be the subject of the image, a numerical value, a theme, etc. Image classification can even use multi-label image classifiers, that work similarly to\u00a0multi-label text classifiers, to tag an image of a stream, for example, into different labels, like \u201cstream,\u201d \u201cwater,\u201d \u201coutdoors,\u201d etc.<\/p>\n<p>Using\u00a0supervised learning algorithms, you can tag images to train your model for appropriate categories. As with all machine learning models, the more you train it, the better it will work.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Machine learning classification uses the mathematically provable guide of algorithms to perform analytical tasks that would take humans hundreds of more hours to perform. And with the proper algorithms in place and a properly trained model, classification programs perform at a level of accuracy that humans could never achieve.<\/p>\n<h4><strong>Related Articles<\/strong><\/h4>\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<div class=\"table-responsive wprt_style_display\">\n<table class=\"table\" dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"329\" \/>\n<col width=\"309\" \/><\/colgroup>\n<tbody>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Syllabus&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-village-field-assistant-vfa-syllabus-exam-pattern\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-regularization-in-machine-learning\/\" target=\"_blank\" rel=\"noopener\">What is Regularization in Machine Learning?<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Mock Test&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-free-mock-test\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/importance-of-data-preprocessing-in-machine-learning\/\" target=\"_blank\" rel=\"noopener\">Importance of Data Preprocessing in Machine Learning<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Exam Date&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-exam-date\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-mining-a-complete-guide\/\" target=\"_blank\" rel=\"noopener\">What Is Data Mining?<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Video Course&quot;}\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/top-data-types-of-python-python-data-types\/\" target=\"_blank\" rel=\"noopener\">Top Data Types of Python<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Application Form&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-apply-online\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\" target=\"_blank\" rel=\"noopener\">What is Data Interpretation? 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There are many different types of classification tasks that you can perform, the most popular being\u00a0sentiment analysis. Each task often requires a different algorithm because each one is used to solve a specific problem. Computer Scientist David Wolpert explains in his paper, The [&hellip;]<\/p>\n","protected":false},"author":111,"featured_media":25534970,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1864],"tags":[],"class_list":["post-25534831","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-data-science-ml"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Introduction to Classification Algorithms in Machine Learning - Entri Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/entri.app\/blog\/introduction-to-classification-algorithms-in-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction to Classification Algorithms in Machine Learning - Entri Blog\" \/>\n<meta property=\"og:description\" content=\"Classification is a natural language processing task that depends on\u00a0machine learning algorithms. There are many different types of classification tasks that you can perform, the most popular being\u00a0sentiment analysis. Each task often requires a different algorithm because each one is used to solve a specific problem. 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