{"id":9634,"date":"2018-02-28T13:07:41","date_gmt":"2018-02-28T13:07:41","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=9634"},"modified":"2025-07-28T15:40:55","modified_gmt":"2025-07-28T10:10:55","slug":"adaboost-algorithm","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/","title":{"rendered":"What is AdaBoost Algorithm &#8211; Model, Prediction, Data Preparation"},"content":{"rendered":"<div>\n<div class=\"\">\n<p>Through this <a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>Machine Learning Tutorial<\/strong><\/a>, we will study the Boosting &#8211; AdaBoost Algorithm. Moreover, we will discuss the AdaBoost Model and Data Preparation.<\/p>\n<p>So, let&#8217;s start the AdaBoost Algorithm Tutorial.<\/p>\n<h3>What is AdaBoost?<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">First of all,<em> AdaBoost is short for Adaptive Boosting<\/em>. Basically, Ada Boosting was the first <span class=\"adverb\">really<\/span> successful boosting algorithm developed for binary classification. Also, it is the best starting point for understanding boosting. Moreover, modern boosting methods build on AdaBoost, most <span class=\"adverb\">notably<\/span> stochastic <a href=\"https:\/\/data-flair.training\/blogs\/gradient-boosting-algorithm\/\"><strong>gradient boosting machines<\/strong><\/a>.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Generally, AdaBoost <span class=\"passivevoice\">is used<\/span> with short decision trees. Further, the first tree <span class=\"passivevoice\">is created<\/span>, the performance of the tree on each training instance <span class=\"passivevoice\">is used<\/span>. Also, we use it to weight how much attention the next tree. Thus, it <span class=\"passivevoice\">is created<\/span> should pay attention to each training instance. Hence, training data that is hard to predict <span class=\"passivevoice\">is given<\/span> more weight. Although, whereas easy to predict instances <span class=\"passivevoice\">are given<\/span> less weight.<\/div>\n<div><\/div>\n<div>In addition to boosting the performance of the model, the AdaBoosting goes through a sequential process that helps the analyst to understand the characteristics of the data. The identification of complex-to-predict cases also guarantees that the resulting model is resilient and can handle well unseen data cases. Moreover, the flexible manner of changing the weights of training instances as carried out in AdaBoost makes it perform well on a number of datasets and problems irrespective of the complexity involved.<\/div>\n<\/div>\n<\/div>\n<div>\n<div class=\"\">\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/future-of-machine-learning\/\"><strong>Read more about Why Learn Machine Learning<\/strong><\/a><\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Learning &#8211; AdaBoost Model<\/h3>\n<p>Learn AdaBoost Model from Data<\/p>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Ada Boosting is best used to boost the performance of decision trees and this\u00a0<span class=\"passivevoice\">is based<\/span> on binary classification problems.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">AdaBoost was <span class=\"adverb\">originally<\/span> called AdaBoost.M1 by the author. More recently it may <span class=\"passivevoice\">be referred<\/span> to as discrete Ada Boost. As because it <span class=\"passivevoice\">is used<\/span> for classification rather than regression.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">AdaBoost can <span class=\"passivevoice\">be used<\/span> to boost the performance of any<a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-algorithm\/\"><strong> machine learning algorithm<\/strong><\/a>. It is best used with weak learners.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Each instance in the training dataset <span class=\"passivevoice\">is weighted<\/span>. The initial weight is set to:<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">weight(xi) = 1\/n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Where xi is the i\u2019th training instance and n is the number of training instances.<\/div>\n<\/div>\n<div><\/div>\n<div>AdaBoost mechanism is particularly unique as it combines the results of weak learners to come up with a strong learner. AdaBoost, on the other hand, corrects the decisions made by previous models and refines them progressively. This ensemble learning reduces the bias and variance and thus yields a good model on different sets of data and uses.<\/div>\n<\/div>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">How To Train One Model?<\/h3>\n<div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">A weak classifier <span class=\"passivevoice\">is prepared<\/span> on the training data using the weighted samples. Only binary classification problems <span class=\"passivevoice\">are supported<\/span>. So each decision stump makes one decision on one input variable. And outputs a +1.0 or -1.0 value for the first or second class value.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The misclassification rate <span class=\"passivevoice\">is calculated<\/span> for the trained model. <span class=\"adverb\">Traditionally<\/span>, this <span class=\"passivevoice\">is calculated<\/span> as:<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">error = (correct \u2013 N) \/ N<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Where error is the misclassification rate. While correct is the number of training instance predicted by the model. And N is the total number of training instances.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/what-is-xgboost\/\">Let&#8217;s discuss what is XGBoost algorithm in detail<\/a><\/strong><\/div>\n<div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Example 1<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">if the model predicted 78 of 100 training instances the error.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This <span class=\"passivevoice\">is modified<\/span> to use the weighting of the training instances:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">error = sum(w(i) * terror(i)) \/ sum(w)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Which is the weighted sum of the misclassification rate.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">where w is the weight for training instance i<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">terror is the prediction error for training instance i. Also, which is 1 if misclassified and 0 if <span class=\"adverb\">correctly<\/span> classified?<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Example 2<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">if we had 3 training instances with the weights 0.01, 0.5 and 0.2.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The predicted values were -1, -1 and -1, and<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">the actual output variables in the instances were -1, 1 and -1, then the terrors would be 0, 1, and 0.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The misclassification rate would <span class=\"passivevoice\">be calculated<\/span> as:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">error = (0.01*0 + 0.5*1 + 0.2*0) \/ (0.01 + 0.5 + 0.2)<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">or<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">error = 0.704<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">A stage value <span class=\"passivevoice\">is calculated<\/span> for the trained model. As it provides a weighting for any predictions that the model makes. The stage value for a trained model <span class=\"passivevoice\">is calculated<\/span> as follows:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">stage = ln((1-error) \/ error)<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Where stage is the stage value used to weight predictions from the model. Also, ln() is the natural logarithm and error is the misclassification error for the model. The effect of the stage weight is that more accurate models have more weight.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The training weights <span class=\"passivevoice\">are updated<\/span> giving more weight to predicted instances. And less weight to predicted instances.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/dimensionality-reduction-tutorial\/\">Read more about Dimensionality Reduction in Detail\u00a0<\/a><\/strong><\/div>\n<div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Example 3<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">the weight of one training instance (w) <span class=\"passivevoice\">is updated<\/span> using:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">w = w * exp(stage * terror)<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Where w is the weight for a specific training instance,<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">exp() is the numerical constant e or Euler\u2019s number raised to a power,<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a stage is the misclassification rate for the weak classifier and<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">terror is the error the weak classifier made predicting the output and evaluated as:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">terror = 0 if(y == p), otherwise 1<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Where y is the output variable for the training instance and p is the prediction from the weak learner.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This has the effect of not changing the weight if the training instance <span class=\"passivevoice\">was classified<\/span>. Thus, making the weight <span class=\"adverb\">slightly<\/span> larger if the weak learner misclassified the instance.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-applications\/\">To learn machine learning applications, follow the below link<\/a><\/strong><\/div>\n<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">AdaBoost Ensemble<\/h3>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Basically, weak models <span class=\"passivevoice\">are added<\/span> <span class=\"adverb\">sequentially<\/span>, trained using the weighted training data.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Generally, the process continues until a pre-set number of weak learners have <span class=\"passivevoice\">been created<\/span>.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Once completed, you <span class=\"passivevoice\">are left<\/span> with a pool of weak learners each with a stage value.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Making Predictions with AdaBoost<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Predictions <span class=\"passivevoice\">are made by<\/span> calculating the weighted average of the weak classifiers.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For a new input instance, each weak learner calculates a predicted value as either +1.0 or -1.0. The predicted values <span class=\"passivevoice\">are weighted by<\/span> each weak learners stage value. The prediction for the ensemble model <span class=\"passivevoice\">is taken<\/span> as a sum of the weighted predictions. If the sum is positive, then the first class <span class=\"passivevoice\">is predicted<\/span>, if negative the second class <span class=\"passivevoice\">is predicted<\/span>.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/advantages-and-disadvantages-of-machine-learning\/\"><strong>Let&#8217;s revise Advantages and Disadvantages of Machine Learning Language<\/strong><\/a><\/div>\n<div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>For example-<\/strong><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">5 weak classifiers may predict the values 1.0, 1.0, -1.0, 1.0, -1.0. From a majority vote, it looks like the model will predict a value of 1.0 or the first class. These same 5 weak classifiers may have the stage values 0.2, 0.5, 0.8, 0.2 and 0.9 <span class=\"adverb\">respectively<\/span>.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Calculating the weighted sum of these predictions results in an output of -0.8. And which would be an ensemble prediction of -1.0 or the second class.<\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Data Preparation for AdaBoost<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This section lists some heuristics for best preparing your data for AdaBoost.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Quality Data:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Because of the ensemble method attempt to correct misclassifications in the training data. Also, you need to be careful that the training data is high-quality.<\/div>\n<\/div>\n<div class=\"\">\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Outliers:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Generally, outliers will force the ensemble down the rabbit hole of work. Although, it is so hard to correct for cases that are unrealistic. These could <span class=\"passivevoice\">be removed<\/span> from the training dataset.<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Noisy Data:<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Basically, noisy data, specifical noise in the output variable can be problematic. But if possible, attempt to isolate and clean these from your training dataset.<\/div>\n<\/div>\n<\/div>\n<div><\/div>\n<div>So, this was all about\u00a0AdaBoost Algorithm in Machine Learning. Hope you like our explanation.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-software\/\"><strong>Read about Best Machine Learning Software in detail<\/strong><\/a><\/div>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Conclusion<\/h3>\n<div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">As a result, we have studied Boosting Algorithm- What is AdaBoost. Also, we have learned Ada boost example. We have also learned Adaboosting applications. I hope this blog will help you to understand the concept of Boosting &#8211; Ada boost.<\/div>\n<\/div>\n<\/div>\n<div><\/div>\n<div>AdaBoost stands for Adaptive Boosting. It works by combining many weak learners into one strong model. A weak learner is a simple model that performs slightly better than random guessing\u2014like a decision stump (a tree with only one decision). AdaBoost gives more focus to the data points that were misclassified in previous rounds and tries to fix them in the next round.<\/div>\n<div><\/div>\n<div>AdaBoost is simple, yet effective. It\u2019s mostly used in classification problems such as face detection, spam filtering, or credit scoring. It works best when used with clean, noise-free data. In Python, AdaBoost can be implemented easily using AdaBoostClassifier from scikit-learn. Though not as fast as XGBoost, it still performs well in many small and medium-sized projects. It\u2019s a great starting point for learning boosting algorithms.<\/div>\n<div><\/div>\n<div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Furthermore, if you have any query, feel free to ask in a comment section.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">See Also-\u00a0<a href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network\/\"><strong>Artificial Neural Network (ANN)<\/strong> <\/a>&amp;\u00a0<strong><a href=\"https:\/\/data-flair.training\/blogs\/neural-network-algorithms\/\">Neural Network Algorithms\u00a0<\/a><\/strong><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/AdaBoost\">For reference<\/a><\/strong><\/div>\n<div><\/div>\n<p><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:2018,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/AdaBoost&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250907013258\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/AdaBoost&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 20:49:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-20 17:33:12&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-28 20:57:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-13 03:28:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-16 13:49:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-28 00:48:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-31 12:36:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-05 02:44:14&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-10 09:42:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-15 12:28:08&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-20 15:08:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-08 21:42:47&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-01 03:04:02&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-08 03:26:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 14:00:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-21 06:42:27&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-26 15:06:30&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-02 18:51:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-10 11:27:39&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-13 12:49:47&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 07:18:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-26 13:04:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-03 21:21:07&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-11 12:07:47&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-11 12:07:47&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Through this Machine Learning Tutorial, we will study the Boosting &#8211; AdaBoost Algorithm. Moreover, we will discuss the AdaBoost Model and Data Preparation. So, let&#8217;s start the AdaBoost Algorithm Tutorial. What is AdaBoost? First&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":9662,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[237,239,240,241,242,244,3395,6338,15546],"class_list":["post-9634","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-ada-boosting","tag-adaboost-algorithm","tag-adaboost-ensemble","tag-adaboost-example","tag-adaboost-model","tag-adaptive-boosting","tag-data-preparation-for-adaboost","tag-how-to-train-one-model","tag-what-is-adaboost"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is AdaBoost Algorithm - Model, Prediction, Data Preparation - DataFlair<\/title>\n<meta name=\"description\" content=\"What is AdaBoost, AdaBoost Algorithm Model, Ada Boosting Ensemble, Making Predictions &amp; Data Preparation for AdaBoost, AdaBoost Example, adaptive boosting\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is AdaBoost Algorithm - Model, Prediction, Data Preparation - DataFlair\" \/>\n<meta property=\"og:description\" content=\"What is AdaBoost, AdaBoost Algorithm Model, Ada Boosting Ensemble, Making Predictions &amp; Data Preparation for AdaBoost, AdaBoost Example, adaptive boosting\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-02-28T13:07:41+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-28T10:10:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Introduction-to-Adaboost-01-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"What is AdaBoost Algorithm - Model, Prediction, Data Preparation - DataFlair","description":"What is AdaBoost, AdaBoost Algorithm Model, Ada Boosting Ensemble, Making Predictions & Data Preparation for AdaBoost, AdaBoost Example, adaptive boosting","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:\/\/data-flair.training\/blogs\/adaboost-algorithm\/","og_locale":"en_US","og_type":"article","og_title":"What is AdaBoost Algorithm - Model, Prediction, Data Preparation - DataFlair","og_description":"What is AdaBoost, AdaBoost Algorithm Model, Ada Boosting Ensemble, Making Predictions & Data Preparation for AdaBoost, AdaBoost Example, adaptive boosting","og_url":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-02-28T13:07:41+00:00","article_modified_time":"2025-07-28T10:10:55+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Introduction-to-Adaboost-01-1.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/7f83c342f5d1632d6f7b4b0b0f447823"},"headline":"What is AdaBoost Algorithm &#8211; Model, Prediction, Data Preparation","datePublished":"2018-02-28T13:07:41+00:00","dateModified":"2025-07-28T10:10:55+00:00","mainEntityOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/"},"wordCount":1373,"commentCount":2,"publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Introduction-to-Adaboost-01-1.jpg","keywords":["Ada Boosting","AdaBoost Algorithm","AdaBoost Ensemble","AdaBoost Example","AdaBoost Model","adaptive boosting","Data Preparation for AdaBoost","How To Train One Model","What is AdaBoost"],"articleSection":["Machine Learning Tutorials"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/","url":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/","name":"What is AdaBoost Algorithm - Model, Prediction, Data Preparation - DataFlair","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/#website"},"primaryImageOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#primaryimage"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Introduction-to-Adaboost-01-1.jpg","datePublished":"2018-02-28T13:07:41+00:00","dateModified":"2025-07-28T10:10:55+00:00","description":"What is AdaBoost, AdaBoost Algorithm Model, Ada Boosting Ensemble, Making Predictions & Data Preparation for AdaBoost, AdaBoost Example, adaptive boosting","breadcrumb":{"@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#primaryimage","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Introduction-to-Adaboost-01-1.jpg","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Introduction-to-Adaboost-01-1.jpg","width":1200,"height":628,"caption":"What is AdaBoost"},{"@type":"BreadcrumbList","@id":"https:\/\/data-flair.training\/blogs\/adaboost-algorithm\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Blog Home","item":"https:\/\/data-flair.training\/blogs\/"},{"@type":"ListItem","position":2,"name":"Machine Learning Tutorials","item":"https:\/\/data-flair.training\/blogs\/category\/machine-learning\/"},{"@type":"ListItem","position":3,"name":"What is AdaBoost Algorithm &#8211; Model, Prediction, Data Preparation"}]},{"@type":"WebSite","@id":"https:\/\/data-flair.training\/blogs\/#website","url":"https:\/\/data-flair.training\/blogs\/","name":"DataFlair","description":"Learn Today. Lead Tomorrow.","publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/data-flair.training\/blogs\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/data-flair.training\/blogs\/#organization","name":"DataFlair","url":"https:\/\/data-flair.training\/blogs\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","width":106,"height":48,"caption":"DataFlair"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataFlairWS\/","https:\/\/x.com\/DataFlairWS","https:\/\/www.linkedin.com\/company\/dataflair-web-services-pvt-ltd\/","https:\/\/www.youtube.com\/user\/DataFlairWS"]},{"@type":"Person","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/7f83c342f5d1632d6f7b4b0b0f447823","name":"DataFlair Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/4cf3a74600d131330b8c481d519afd1574093ed89f6d3396a95393ad223eb7cd?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/4cf3a74600d131330b8c481d519afd1574093ed89f6d3396a95393ad223eb7cd?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/4cf3a74600d131330b8c481d519afd1574093ed89f6d3396a95393ad223eb7cd?s=96&d=mm&r=g","caption":"DataFlair Team"},"description":"DataFlair Team creates expert-level guides on programming, Java, Python, C++, DSA, AI, ML, data Science, Android, Flutter, MERN, Web Development, and technology. Our goal is to empower learners with easy-to-understand content. Explore our resources for career growth and practical learning.","url":"https:\/\/data-flair.training\/blogs\/author\/dfteam1\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/9634","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/comments?post=9634"}],"version-history":[{"count":7,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/9634\/revisions"}],"predecessor-version":[{"id":146268,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/9634\/revisions\/146268"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media\/9662"}],"wp:attachment":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media?parent=9634"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/categories?post=9634"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/tags?post=9634"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}