{"id":25530263,"date":"2022-06-26T01:30:54","date_gmt":"2022-06-25T20:00:54","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25530263"},"modified":"2022-11-23T12:48:32","modified_gmt":"2022-11-23T07:18:32","slug":"how-to-implement-classification-in-machine-learning","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/","title":{"rendered":"How to Implement Classification 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-69e09fbea4e6e\" 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-69e09fbea4e6e\"  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\/how-to-implement-classification-in-machine-learning\/#What_is_Classification_In_Machine_Learning\" >What is Classification In Machine Learning\u00a0<\/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\/how-to-implement-classification-in-machine-learning\/#Classification_Terminologies_In_Machine_Learning\" >Classification Terminologies 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\/how-to-implement-classification-in-machine-learning\/#Classification_Algorithms\" >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\/how-to-implement-classification-in-machine-learning\/#Naive_Bayes_Classifier\" >Naive Bayes Classifier<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/#Classifier_Evaluation\" >Classifier Evaluation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/#Algorithm_Selection\" >Algorithm Selection<\/a><\/li><\/ul><\/nav><\/div>\n<p>Classification in machine learning and statistics is a supervised learning approach in which the computer program learns from the data given to it and make new observations or classifications. In this article, we will learn about classification in machine learning in detail.<\/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><span class=\"ez-toc-section\" id=\"What_is_Classification_In_Machine_Learning\"><\/span><strong>What is Classification In Machine Learning\u00a0<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories.<\/p>\n<p>The classification predictive modeling is the task of approximating the mapping function from input variables to discrete output variables. The main goal is to identify which class\/category the new data will fall into.<\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122186 size-full\" src=\"https:\/\/www.edureka.co\/blog\/wp-content\/uploads\/2019\/11\/classification.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/classification.png 273w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/classification-150x103.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/classification-263x180.png 263w\" alt=\"Classification In Machine Learning\" width=\"273\" height=\"187\" \/><\/strong><\/p>\n<p>Let us try to understand this with a simple example.<\/p>\n<p>Heart disease detection can be identified as a classification problem, this is a binary classification since there can be only two classes i.e has heart disease or does not have heart disease. The classifier, in this case, needs training data to understand how the given input variables are related to the class. And once the classifier is trained accurately, it can be used to detect whether heart disease is there or not for a particular patient.<\/p>\n<p>Let us get familiar with the classification in machine learning terminologies.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/bit.ly\/3ELmCiA\" target=\"_blank\" rel=\"noopener\">Learn Machine learning in advanced level. Join Entri now<\/a><\/h4>\n<h2><span class=\"ez-toc-section\" id=\"Classification_Terminologies_In_Machine_Learning\"><\/span><strong>Classification Terminologies In Machine Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Classifier\u00a0<\/strong>\u2013 It is an algorithm that is used to map the input data to a specific category.<\/li>\n<li><strong>Classification Model\u00a0<\/strong>\u2013 The model predicts or draws a conclusion to the input data given for training, it will predict the class or category for the data.<\/li>\n<li><strong>Feature<\/strong>\u00a0\u2013 A feature is an individual measurable property of the phenomenon being observed.<\/li>\n<li><strong>Binary\u00a0 Classification\u00a0<\/strong>\u2013 It is a type of classification with two outcomes, for eg \u2013 either true or false.<\/li>\n<li><strong>Multi-Class Classification<\/strong>\u00a0\u2013 The classification with more than two classes, in multi-class classification each sample is assigned to one and only one label or target.<\/li>\n<li><strong>Multi-label Classification\u00a0<\/strong>\u2013 This is a type of classification where each sample is assigned to a set of labels or targets.<\/li>\n<li><strong>Initialize\u00a0<\/strong>\u2013 It is to assign the classifier to be used for the<\/li>\n<li><strong>Train the Classifier<\/strong>\u00a0\u2013 Each classifier in sci-kit learn uses the fit(X, y) method to fit the model for training the train X and train label y.<\/li>\n<li><strong>Predict the Target\u00a0<\/strong>\u2013 For an unlabeled observation X, the predict(X) method returns predicted label y.<\/li>\n<li><strong>Evaluate<\/strong>\u00a0\u2013 This basically means the evaluation of the model i.e classification report, accuracy score, etc.<\/li>\n<\/ul>\n<h3><strong>Types Of Learners In Classification<\/strong><\/h3>\n<ul>\n<li><strong>Lazy Learners<\/strong>\u00a0\u2013 Lazy learners simply store the training data and wait until a testing data appears. The classification is done using the most related data in the stored training data. They have more predicting time compared to eager learners. Eg \u2013 k-nearest neighbor, case-based reasoning.<\/li>\n<li><strong>Eager Learners<\/strong> \u2013 Eager learners construct a classification model based on the given training data before getting data for predictions. It must be able to commit to a single hypothesis that will work for the entire space. Due to this, they take a lot of time in training and less time for a prediction. For example : <a name=\"algo\"><\/a>Decision Tree, Naive Bayes, Artificial Neural Networks.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Classification_Algorithms\"><\/span><strong>Classification Algorithms<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In machine learning, classification is a supervised learning concept which basically categorizes a set of data into classes. The most common classification problems are \u2013\u00a0speech recognition,\u00a0face detection, handwriting recognition, document classification, etc. It can be either a binary classification problem or a multi-class problem too. There are a bunch of\u00a0machine learning algorithms\u00a0for classification in machine learning.\u00a0<a name=\"log\"><\/a>Let us take a look at those classification algorithms in machine learning.<\/p>\n<h3><strong>Logistic Regression<\/strong><\/h3>\n<p>It is a classification algorithm in machine learning that uses one or more independent variables to determine an outcome. The outcome is measured with a dichotomous variable which means\u00a0it will have only two possible outcomes.<\/p>\n<p>The goal of logistic regression is to find a best-fitting relationship between the dependent variable and a set of independent variables. It is better than other binary classification algorithms like nearest neighbor since it quantitatively explains the factors leading to classification.<\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122187 size-full blur-up lazyloaded\" src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log.png 250w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log-150x145.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log-186x180.png 186w\" alt=\"\" width=\"250\" height=\"242\" data-src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log.png\" data-srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log.png 250w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log-150x145.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/log-186x180.png 186w\" data-sizes=\"(max-width: 250px) 100vw, 250px\" \/><\/strong><\/p>\n<h4><strong>Advantages and Disadvantages<\/strong><\/h4>\n<p>Logistic regression is specifically meant for classification, it is useful in understanding how a set of independent variables affect the outcome of the dependent variable.<\/p>\n<p>The main disadvantage of the logistic regression algorithm is that it only works when the predicted variable is binary, it assumes that the data is free of missing values and assumes that the predictors are independent of each other.<\/p>\n<h4><strong>Use Cases<\/strong><\/h4>\n<ul>\n<li>Identifying risk factors for diseases<\/li>\n<li>Word classification<\/li>\n<li>Weather Prediction<\/li>\n<li>Voting Applications<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Naive_Bayes_Classifier\"><\/span><strong>Naive Bayes Classifier<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>It is a classification algorithm based on\u00a0Bayes\u2019s theorem\u00a0which gives an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.<\/p>\n<p>Even if the features depend on each other, all of these properties contribute to the probability independently. Naive Bayes model is easy to make and is particularly useful for comparatively large data sets. Even with a simplistic approach, Naive Bayes is known to outperform most of the classification methods in machine learning. Following is the Bayes theorem to implement the Naive Bayes Theorem.<\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122180 size-full blur-up lazyloaded\" src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive.png 744w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-150x20.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-300x40.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-528x70.png 528w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-590x78.png 590w\" alt=\"\" width=\"744\" height=\"98\" data-src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive.png\" data-srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive.png 744w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-150x20.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-300x40.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-528x70.png 528w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/naive-590x78.png 590w\" data-sizes=\"(max-width: 744px) 100vw, 744px\" \/><\/strong><\/p>\n<h4><strong>Advantages and Disadvantages<\/strong><\/h4>\n<p>The Naive Bayes classifier requires a small amount of training data to estimate the necessary parameters to get the results. They are extremely fast in nature compared to other classifiers.<\/p>\n<p>The only disadvantage is that they are known to be a bad estimator.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/bit.ly\/3ELmCiA\" target=\"_blank\" rel=\"noopener\">Learn Machine learning in advanced level. Join Entri now<\/a><\/h4>\n<h4><strong>Use Cases<\/strong><\/h4>\n<ul>\n<li>Disease Predictions<\/li>\n<li>Document Classification<\/li>\n<li>Spam Filters<\/li>\n<li><a name=\"gradient\"><\/a>Sentiment Analysis<\/li>\n<\/ul>\n<h3><strong>Stochastic Gradient Descent<\/strong><\/h3>\n<p>It is a very effective and simple approach to fit linear models. Stochastic Gradient Descent is particularly useful when the\u00a0sample data is in a large number. It supports different loss functions and penalties for classification.<\/p>\n<p>Stochastic gradient descent refers to calculating the derivative from each training data instance and calculating the update immediately.<\/p>\n<h4><strong>Advantages and Disadvantages<\/strong><\/h4>\n<p>The only advantage is the ease of implementation and efficiency whereas a major setback with stochastic gradient descent is that it requires a number of hyper-parameters and is sensitive to feature scaling.<\/p>\n<h4><strong>Use Cases<\/strong><\/h4>\n<ul>\n<li>Internet Of Things<\/li>\n<li><a name=\"knn\"><\/a>Updating the parameters such as weights in neural networks or coefficients in linear regression<\/li>\n<\/ul>\n<h3><strong>K-Nearest Neighbor<\/strong><\/h3>\n<p>It is a lazy learning algorithm that\u00a0stores all instances corresponding to training data in n-dimensional space. It is a\u00a0lazy learning algorithm\u00a0as it does not focus on constructing a general internal model, instead, it works on storing instances of training data.<\/p>\n<p>Classification is computed from a simple majority vote of the k nearest neighbors of each point. It is supervised and takes a bunch of labelled points and uses them to label other points. To label a new point, it looks at the labelled points closest to that new point also known as its nearest neighbors. It has those neighbors vote, so whichever label most of the neighbors have is the label for the new point. The \u201ck\u201d is the number of neighbors it checks.<\/p>\n<h4><strong>Advantages And Disadvantages<\/strong><\/h4>\n<p>This algorithm is quite simple in its implementation and is robust to noisy training data. Even if the training data is large, it is quite efficient. The only disadvantage with the KNN algorithm is that there is no need to determine the value of K and computation cost is pretty high compared to other algorithms.<\/p>\n<h4><strong>Use Cases<\/strong><\/h4>\n<ul>\n<li>Industrial applications to look for similar tasks in comparison to others<\/li>\n<li>Handwriting detection applications<\/li>\n<li>Image recognition<\/li>\n<li>Video recognition<\/li>\n<li><a name=\"tree\"><\/a>Stock analysis<\/li>\n<\/ul>\n<h3><strong>Decision Tree<\/strong><\/h3>\n<p>The decision tree algorithm builds the classification model in the form of a\u00a0tree structure. It utilizes the if-then rules which are equally exhaustive and mutually exclusive in classification. The process goes on with breaking down the data into smaller structures and eventually associating it with an incremental decision tree. The final structure looks like a tree with nodes and leaves. The\u00a0rules are learned sequentially\u00a0using the training data one at a time. Each time a rule is learned, the tuples covering the rules are removed. The process continues on the training set until the termination point is met.<\/p>\n<p>The tree is constructed in a top-down recursive divide and conquer approach. A decision node will have two or more branches and a leaf represents a classification or decision. The topmost node in the decision tree that corresponds to the best predictor is called the root node, and the best thing about a decision tree is that it can handle both categorical and numerical data.<\/p>\n<h4><strong>Advantages and Disadvantages<\/strong><\/h4>\n<p>A decision tree gives an advantage of simplicity to understand and visualize, it requires very little data preparation as well. The disadvantage that follows with the decision tree is that it can create complex trees that may not categorize efficiently. They can be quite unstable because even a simplistic change in the data can hinder the whole structure of the decision tree.<\/p>\n<h4><strong>Use Cases<\/strong><\/h4>\n<ul>\n<li>Data exploration<\/li>\n<li>Pattern Recognition<\/li>\n<li>Option pricing in finances<\/li>\n<li>Identifying disease and risk threats<\/li>\n<\/ul>\n<h3><strong>Random Forest<\/strong><\/h3>\n<p>Random decision trees or random forest are an\u00a0ensemble learning method\u00a0for classification, regression, etc. It operates by constructing a multitude of decision trees at training time and outputs the class that is the mode of the classes or classification or mean prediction(regression) of the individual trees.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122183 size-full blur-up lazyloaded\" src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random.png 281w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random-150x131.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random-206x180.png 206w\" alt=\"random forest - classification in machine learning - edureka\" width=\"281\" height=\"245\" data-src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random.png\" data-srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random.png 281w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random-150x131.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/random-206x180.png 206w\" data-sizes=\"(max-width: 281px) 100vw, 281px\" \/><\/p>\n<p>A random forest is a meta-estimator that fits a number of trees on various subsamples of data sets and then uses an average to improve the accuracy in the model\u2019s predictive nature. The sub-sample size is always the same as that of the original input size but the samples are often drawn with replacements.<\/p>\n<h4><strong>Advantages and Disadvantages<\/strong><\/h4>\n<p>The advantage of the random forest is that it is more accurate than the decision trees due to the reduction in the over-fitting. The only disadvantage with the random forest classifiers is that it is quite complex in implementation and gets pretty slow in real-time prediction.<\/p>\n<h4><strong>Use Cases<\/strong><\/h4>\n<ul>\n<li>Industrial applications such as finding if a loan applicant is high-risk or low-risk<\/li>\n<li>For Predicting the failure of\u00a0 mechanical parts in automobile engines<\/li>\n<li>Predicting social media share scores<\/li>\n<li>Performance scores<\/li>\n<\/ul>\n<h3><strong>Support Vector Machine<\/strong><\/h3>\n<p>The support vector machine is a classifier that represents the\u00a0training data as points in space\u00a0separated into categories by a gap as wide as possible. New points are then added to space by predicting which category they fall into and which space they will belong to.<\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122168 size-full blur-up lazyloaded\" src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1.png 671w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-150x97.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-300x195.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-462x300.png 462w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-277x180.png 277w\" alt=\"svm - classification in machine learning - edureka\" width=\"671\" height=\"436\" data-src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1.png\" data-srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1.png 671w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-150x97.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-300x195.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-462x300.png 462w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/svm-2-1-277x180.png 277w\" data-sizes=\"(max-width: 671px) 100vw, 671px\" \/><\/strong><\/p>\n<h4><strong>Advantages and Disadvantages<\/strong><\/h4>\n<p>It uses a subset of training points in the decision function which makes it memory efficient and is highly effective in high dimensional spaces. The only disadvantage with the support vector machine is that the algorithm does not directly provide probability estimates.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Classifier_Evaluation\"><\/span><strong>Classifier Evaluation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The most important part after the completion of any classifier is the evaluation to check its accuracy and efficiency. There are a lot of ways in which we can evaluate a classifier. Let us take a look at these methods listed below.<\/p>\n<h3><strong>Holdout Method<\/strong><\/h3>\n<p>This is the most common method to evaluate a classifier. In this method, the given data set is divided into two parts as a test and train set 20% and 80% respectively.<\/p>\n<p>The train set is used to train the data and the unseen test set is used to test its predictive power.<\/p>\n<h3><strong>Cross-Validation<\/strong><\/h3>\n<p>Over-fitting is the most common problem prevalent in most of the machine learning models. K-fold cross-validation can be conducted to verify if the model is over-fitted at all.<\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122188 size-full blur-up lazyloaded\" src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross.png 305w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross-150x117.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross-300x234.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross-231x180.png 231w\" alt=\"\" width=\"305\" height=\"238\" data-src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross.png\" data-srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross.png 305w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross-150x117.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross-300x234.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/cross-231x180.png 231w\" data-sizes=\"(max-width: 305px) 100vw, 305px\" \/><\/strong><\/p>\n<p>In this method, the data set is randomly partitioned into\u00a0k mutually exclusive\u00a0subsets, each of which is of the same size. Out of these, one is kept for testing and others are used to train the model. The same process takes place for all k folds.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Algorithm_Selection\"><\/span><strong>Algorithm Selection<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-122201 size-large blur-up lazyloaded\" src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-307x300.png\" srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-307x300.png 307w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-150x147.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-300x294.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-768x752.png 768w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-184x180.png 184w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6.png 940w\" alt=\"\" width=\"307\" height=\"300\" data-src=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-307x300.png\" data-srcset=\"https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-307x300.png 307w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-150x147.png 150w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-300x294.png 300w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-768x752.png 768w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6-184x180.png 184w, https:\/\/d1jnx9ba8s6j9r.cloudfront.net\/blog\/wp-content\/uploads\/2019\/11\/Blank-Diagram-6.png 940w\" data-sizes=\"(max-width: 307px) 100vw, 307px\" \/><\/p>\n<p>Apart from the above approach, We can follow the following steps to use the best algorithm for the model<\/p>\n<ul>\n<li>Read the data<\/li>\n<li>Create dependent and independent data sets based on our dependent and independent features<\/li>\n<li>Split the data into training and testing sets<\/li>\n<li>Train the model using different algorithms such as KNN, Decision tree, SVM, etc<\/li>\n<li>Evaluate the classifier<\/li>\n<li>Choose the classifier with the most accuracy.<\/li>\n<\/ul>\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<p><a name=\"usecase\"><\/a>Although it may take more time than needed to choose the best algorithm suited for your model, accuracy is the best way to go forward to make your model efficient.<\/p>\n<h4 style=\"text-align: center;\"><a href=\"https:\/\/bit.ly\/3ELmCiA\" target=\"_blank\" rel=\"noopener\">Learn Machine learning in advanced level. Join Entri now<\/a><\/h4>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Classification in machine learning and statistics is a supervised learning approach in which the computer program learns from the data given to it and make new observations or classifications. In this article, we will learn about classification in machine learning in detail. What is Classification In Machine Learning\u00a0 Classification is a process of categorizing a [&hellip;]<\/p>\n","protected":false},"author":111,"featured_media":25530348,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1864],"tags":[],"class_list":["post-25530263","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>How to Implement Classification 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\/how-to-implement-classification-in-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Implement Classification In Machine Learning? - Entri Blog\" \/>\n<meta property=\"og:description\" content=\"Classification in machine learning and statistics is a supervised learning approach in which the computer program learns from the data given to it and make new observations or classifications. In this article, we will learn about classification in machine learning in detail. What is Classification In Machine Learning\u00a0 Classification is a process of categorizing a [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"Entri Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/entri.me\/\" \/>\n<meta property=\"article:published_time\" content=\"2022-06-25T20:00:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-11-23T07:18:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/06\/How-to-Implement-Classification-In-Machine-Learning.png\" \/>\n\t<meta property=\"og:image:width\" content=\"820\" \/>\n\t<meta property=\"og:image:height\" content=\"615\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Feeba Mahin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@entri_app\" \/>\n<meta name=\"twitter:site\" content=\"@entri_app\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Feeba Mahin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/\"},\"author\":{\"name\":\"Feeba Mahin\",\"@id\":\"https:\/\/entri.app\/blog\/#\/schema\/person\/f036dab84abae3dcc9390a1110d95d36\"},\"headline\":\"How to Implement Classification In Machine Learning?\",\"datePublished\":\"2022-06-25T20:00:54+00:00\",\"dateModified\":\"2022-11-23T07:18:32+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/\"},\"wordCount\":2082,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/entri.app\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/06\/How-to-Implement-Classification-In-Machine-Learning.png\",\"articleSection\":[\"Articles\",\"Data Science and Machine Learning\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/\",\"url\":\"https:\/\/entri.app\/blog\/how-to-implement-classification-in-machine-learning\/\",\"name\":\"How to Implement Classification In Machine Learning? 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