{"id":15100,"date":"2022-01-09T12:05:00","date_gmt":"2022-01-09T06:35:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/adaboost-algorithm\/"},"modified":"2025-12-01T14:02:32","modified_gmt":"2025-12-01T08:32:32","slug":"adaboost-algorithm","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/adaboost-algorithm\/","title":{"rendered":"The Ultimate Guide to AdaBoost Algorithm"},"content":{"rendered":"\n<p>In this guide, we are going to show you exactly how AdaBoost works, step-by-step.<\/p>\n\n\n\n<p>We are going to cover:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The mathematical formulas behind the algorithm.<\/li>\n\n\n\n<li>The concept of \"Weak Learners\" and Decision Stumps.<\/li>\n\n\n\n<li>The critical \"Weight Update\" and \"Bucketing\" mechanisms.<\/li>\n\n\n\n<li>How to implement it in Python.<\/li>\n<\/ul>\n\n\n\n<p>Let's dive into mastering this fundamental Machine Learning technique.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-is-adaboost\">What is AdaBoost?<\/h2>\n\n\n\n<p>AdaBoost (short for <strong>Adaptive Boosting<\/strong>) is a <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-supervised-machine-learning\/\">supervised<\/a> machine learning algorithm used for classification.<\/p>\n\n\n\n<p>It is part of a family of algorithms known as <strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/bagging-boosting\/\">Ensemble Methods<\/a><\/strong>.<\/p>\n\n\n\n<p>But here is the thing that makes AdaBoost unique:<\/p>\n\n\n\n<p>Unlike Random Forest, which builds trees in parallel (Bagging), AdaBoost builds models <strong>sequentially<\/strong> (Boosting).<\/p>\n\n\n\n<p>It converts a set of \"Weak Learners\" into a single \"Strong Learner.\"<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"the-core-concept-adaptive-learning\">The Core Concept: \"Adaptive\" Learning<\/h3>\n\n\n\n<p>Why is it called <em>Adaptive<\/em>?<\/p>\n\n\n\n<p>Because the algorithm adjusts itself after every iteration.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>It builds a model.<\/li>\n\n\n\n<li>It identifies the errors (misclassified data).<\/li>\n\n\n\n<li>It <strong>adapts<\/strong> by assigning higher weights to those errors.<\/li>\n\n\n\n<li>The next model focuses exclusively on fixing those errors.<\/li>\n<\/ol>\n\n\n\n<p>This iterative process reduces <strong>bias<\/strong>, allowing the model to capture complex patterns that a single Decision Tree would miss.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"the-bias-vs-variance-trade-off\">The \"Bias vs. Variance\" Trade-off<\/h3>\n\n\n\n<p>To understand why AdaBoost is so effective, you need to understand the<a href=\"https:\/\/www.mygreatlearning.com\/blog\/bias-variance-tradeoff-in-machine-learning\/\"> two main sources of error<\/a> in Machine Learning:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bias:<\/strong> Error caused by overly simple assumptions (underfitting).<\/li>\n\n\n\n<li><strong>Variance:<\/strong> Error caused by too much complexity (overfitting).<\/li>\n<\/ul>\n\n\n\n<p>AdaBoost is unique because it focuses on reducing <strong>Bias<\/strong>. Unlike Random Forest, which builds deep trees in parallel to reduce variance, AdaBoost builds shallow stumps sequentially to aggressively fix errors.<\/p>\n\n\n\n    <div class=\"courses-cta-container\">\n        <div class=\"courses-cta-card\">\n            <div class=\"courses-cta-header\">\n                <div class=\"courses-learn-icon\"><\/div>\n                <span class=\"courses-learn-text\">Academy Pro<\/span>\n            <\/div>\n            <p class=\"courses-cta-title\">\n                <a href=\"https:\/\/www.mygreatlearning.com\/academy\/premium\/machine-learning-essentials-with-python\" class=\"courses-cta-title-link\">Learn Machine Learning with Python<\/a>\n            <\/p>\n            <p class=\"courses-cta-description\">Learn machine learning with Python! Master the basics, build models, and unlock the power of data to solve real-world challenges.<\/p>\n            <div class=\"courses-cta-stats\">\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-user-icon\"><\/div>\n                    <span>12 Hrs<\/span>\n                <\/div>\n                <div class=\"courses-stat-item\">\n                    <div class=\"courses-stat-icon courses-star-icon\"><\/div>\n                    <span>1 Coding Exercise<\/span>\n                <\/div>\n            <\/div>\n            <a href=\"https:\/\/www.mygreatlearning.com\/academy\/premium\/machine-learning-essentials-with-python\" class=\"courses-cta-button\">\n                Learn Machine Learning with Python\n                <div class=\"courses-arrow-icon\"><\/div>\n            <\/a>\n        <\/div>\n    <\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-architecture-decision-stumps\">The Architecture (Decision Stumps)<\/h2>\n\n\n\n<p>To understand AdaBoost, you first need to understand the <strong>Decision Stump<\/strong>.<\/p>\n\n\n\n<p>Most people think AdaBoost uses <a href=\"https:\/\/www.mygreatlearning.com\/blog\/decision-tree-algorithm\/\">Decision Trees<\/a>. That is technically true, but they are not the deep trees you see in a Random Forest.<\/p>\n\n\n\n<p>They are <strong>Stumps<\/strong>.<\/p>\n\n\n\n<p>A Decision Stump is a tree with a <code>max_depth<\/code> of 1.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>It has one root node.<\/li>\n\n\n\n<li>It has two leaf nodes.<\/li>\n\n\n\n<li>It performs a single split on a single feature.<\/li>\n<\/ul>\n\n\n\n<p><strong>This is the definition of a Weak Learner.<\/strong> A stump is only slightly better than random guessing.<\/p>\n\n\n\n<p>However, AdaBoost combines hundreds or thousands of these stumps to create a highly accurate prediction engine.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-adaboost-works-the-step-by-step-process\">How AdaBoost Works (The Step-by-Step Process)<\/h2>\n\n\n\n<p>This is the most important part of the guide.<\/p>\n\n\n\n<p>We are going to walk through the exact mathematical process AdaBoost uses to train a model.<\/p>\n\n\n\n<p>Let\u2019s imagine we have a dataset with <strong>5 records<\/strong> (Samples).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-1-assign-initial-weights\">Step #1: Assign Initial Weights<\/h3>\n\n\n\n<p>When the algorithm starts, every sample is equal.<\/p>\n\n\n\n<p>We assign a Sample Weight (<em>w<\/em>) to every row in the dataset using this formula:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?w_i%20%3D%20%5Cfrac%7B1%7D%7BN%7D\" alt=\"Weight Formula\"\/><\/figure>\n\n\n\n<p>Where <em>N<\/em> is the total number of records.<br>Since we have 5 records, every row starts with a weight of <strong>0.2<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-2-create-the-first-base-learner\">Step #2: Create the First Base Learner<\/h3>\n\n\n\n<p>The algorithm looks at all the features and creates the first Decision Stump.<\/p>\n\n\n\n<p>It selects the stump that has the lowest <strong>Gini Impurity<\/strong> or <strong>Entropy<\/strong>.<\/p>\n\n\n\n<p>Let's say <strong>Stump 1<\/strong> predicts 4 records correctly and 1 record incorrectly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-3-calculate-the-total-error-te\">Step #3: Calculate the Total Error (TE)<\/h3>\n\n\n\n<p>This is where many people get confused.<\/p>\n\n\n\n<p>The Total Error is <strong>not<\/strong> just the number of wrong guesses. It is the sum of the <strong>weights<\/strong> of the misclassified samples.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?TE%20%3D%20%5Csum%20%28%5text%7BWeights%20of%20Misclassified%20Records%7D%29\" alt=\"Total Error Formula\"\/><\/figure>\n\n\n\n<p>Since the weight of our one wrong record is 0.2:<br><strong>TE = 0.2<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-4-calculate-amount-of-say-alpha\">Step #4: Calculate \"Amount of Say\" (Alpha)<\/h3>\n\n\n\n<p>Now we need to calculate how important this stump is. This is called the <strong>Amount of Say<\/strong> (denoted by Alpha, \u03b1).<\/p>\n\n\n\n<p>We use this formula:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?%5Calpha%20%3D%20%5Cfrac%7B1%7D%7B2%7D%20%5Cln%20%5Cleft%28%20%5Cfrac%7B1%20-%20TE%7D%7BTE%7D%20%5Cright%29\" alt=\"Alpha Formula\"\/><\/figure>\n\n\n\n<p>Let\u2019s plug in our error (0.2):<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>(1 - 0.2) \/ 0.2 = 4<\/li>\n\n\n\n<li>ln(4) \u2248 1.386<\/li>\n\n\n\n<li>0.5 \u00d7 1.386 \u2248 0.693<\/li>\n<\/ol>\n\n\n\n<p>So, the Alpha (\u03b1) for this stump is <strong>0.693<\/strong>.<\/p>\n\n\n\n<p><strong>The Key Takeaway:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Low Error:<\/strong> High Alpha (Positive). The stump has a strong vote.<\/li>\n\n\n\n<li><strong>High Error (0.5):<\/strong> Zero Alpha. The stump has no vote.<\/li>\n\n\n\n<li><strong>Random Guessing:<\/strong> If error is 50%, the stump is useless.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-5-update-the-weights\">Step #5: Update the Weights<\/h3>\n\n\n\n<p>This is the \"Boosting\" part. We need to tell the <em>next<\/em> stump which records are difficult.<\/p>\n\n\n\n<p>We update the weights for <strong>incorrectly classified<\/strong> records using this formula:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?w_%7Bnew%7D%20%3D%20w_%7Bold%7D%20%5Ctimes%20e%5E%7B%5Calpha%7D\" alt=\"Weight Update Incorrect\"\/><\/figure>\n\n\n\n<p>Since \u03b1 is positive, <em>e<sup>\u03b1<\/sup><\/em> is greater than 1.<br><strong>Result:<\/strong> The weight increases.<\/p>\n\n\n\n<p>We update the weights for <strong>correctly classified<\/strong> records using this formula:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?w_%7Bnew%7D%20%3D%20w_%7Bold%7D%20%5Ctimes%20e%5E%7B-%5Calpha%7D\" alt=\"Weight Update Correct\"\/><\/figure>\n\n\n\n<p><strong>Result:<\/strong> The weight decreases.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-6-normalize-the-weights\">Step #6: Normalize the Weights<\/h3>\n\n\n\n<p>After the update, the weights will no longer sum up to 1. To fix this, we divide every weight by the sum of the new weights.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?w_%7Bnormalized%7D%20%3D%20%5Cfrac%7Bw_%7Bnew%7D%7D%7B%5Csum%20w_%7Bnew%7D%7D\" alt=\"Normalization Formula\"\/><\/figure>\n\n\n\n<p>Now, the difficult record (the one we got wrong) has a much higher probability (weight) than the easy records.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-7-the-bucket-method-resampling\">Step #7: The Bucket Method (Resampling)<\/h3>\n\n\n\n<p>This is the mechanism AdaBoost uses to pass the data to the next learner.<\/p>\n\n\n\n<p>We create a <strong>new dataset<\/strong> of size <em>N<\/em> (5 records) by resampling the original data.<\/p>\n\n\n\n<p>We use the <strong>Bucket Method<\/strong>:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>We create \"buckets\" based on the normalized weights.<\/li>\n\n\n\n<li>The record with the high weight gets a large bucket (e.g., from 0.0 to 0.50).<\/li>\n\n\n\n<li>The records with low weights get tiny buckets (e.g., from 0.50 to 0.55).<\/li>\n\n\n\n<li>We pick a random number between 0 and 1.<\/li>\n<\/ol>\n\n\n\n<p>Because the difficult record has a massive bucket, the random number will likely fall inside it multiple times.<\/p>\n\n\n\n<p><strong>The Result:<\/strong> The new dataset contains <strong>multiple copies<\/strong> of the difficult record.<\/p>\n\n\n\n<p>When the next Decision Stump tries to minimize errors on this new dataset, it <em>must<\/em> classify that difficult record correctly, or its error rate will be huge.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"making-the-final-prediction\">Making the Final Prediction<\/h2>\n\n\n\n<p>Once we have trained all our stumps (let's say 50 of them), how do we make a prediction on test data?<\/p>\n\n\n\n<p>We use a <strong>Weighted Majority Vote<\/strong>.<\/p>\n\n\n\n<p>We calculate the prediction for a new data point, <em>x<\/em>, using this sign function:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" src=\"https:\/\/latex.codecogs.com\/png.latex?H%28x%29%20%3D%20%5text%7Bsign%7D%20%5Cleft%28%20%5Csum_%7Bt%3D1%7D%5E%7BT%7D%20%5Calpha_t%20h_t%28x%29%20%5Cright%29\" alt=\"Final Prediction Formula\"\/><\/figure>\n\n\n\n<p>Here is what happens in plain English:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Every stump makes a prediction (+1 or -1).<\/li>\n\n\n\n<li>We multiply each prediction by that stump's Alpha (\u03b1).<\/li>\n\n\n\n<li>We sum them all up.<\/li>\n\n\n\n<li>If the result is positive, we predict Class 1. If negative, Class -1.<\/li>\n<\/ol>\n\n\n\n<p>This ensures that the \"smart\" stumps (high Alpha) count more than the \"weak\" stumps.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"python-implementation\">Python Implementation<\/h2>\n\n\n\n<p>Implementing AdaBoost is straightforward using the <code>scikit-learn<\/code> library.<\/p>\n\n\n\n<p>Here is a clean, production-ready code snippet.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# 1. Generate a sample dataset\nX, y = make_classification(n_samples=1000, n_features=20, random_state=42)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n\n# 2. Define the Weak Learner (Stump)\n# We strictly set max_depth to 1\nstump = DecisionTreeClassifier(max_depth=1, max_features=&#039;auto&#039;)\n\n# 3. Initialize AdaBoost\n# n_estimators: The number of stumps to create\n# learning_rate: Controls the contribution of each model\nada = AdaBoostClassifier(\n    base_estimator=stump,\n    n_estimators=50,\n    learning_rate=1.0,\n    algorithm=&#039;SAMME&#039;\n)\n\n# 4. Fit the model\nada.fit(X_train, y_train)\n\n# 5. Evaluate\npredictions = ada.predict(X_test)\nprint(f&quot;Model Accuracy: {accuracy_score(y_test, predictions)}&quot;)\n\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"key-hyperparameters\">Key Hyperparameters<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><code>n_estimators<\/code><\/strong>: The number of stumps. Increasing this generally improves performance but increases training time.<\/li>\n\n\n\n<li><strong><code>learning_rate<\/code><\/strong>: This shrinks the contribution of each tree. There is a trade-off: if you lower the learning rate, you usually need to increase <code>n_estimators<\/code>.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"pros-and-cons\">Pros and Cons<\/h2>\n\n\n\n<p>AdaBoost is powerful, but it is not a silver bullet. You need to know when to use it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"the-advantages\">The Advantages<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>High Accuracy:<\/strong> It pushes the limits of weak learners.<\/li>\n\n\n\n<li><strong>No Parameter Tuning:<\/strong> Compared to SVM or Neural Networks, AdaBoost works well \"out of the box.\"<\/li>\n\n\n\n<li><strong>Feature Selection:<\/strong> It implicitly identifies important features by ignoring irrelevant ones during stump creation.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"the-disadvantages-the-pain-point\">The Disadvantages (The Pain Point)<\/h3>\n\n\n\n<p>There is one major issue you must be aware of: <strong>Outliers.<\/strong><\/p>\n\n\n\n<p>Because AdaBoost minimizes the <strong>Exponential Loss Function<\/strong>, it applies massive weights to misclassified samples.<\/p>\n\n\n\n<p>If your dataset has noisy outliers (garbage data), AdaBoost will obsess over them. It will ruin the model by trying to fit points that shouldn't be fitted.<\/p>\n\n\n\n<p><strong>Pro Tip:<\/strong> If your data is noisy, use <strong>Gradient Boosting<\/strong> or <strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/random-forest-algorithm\/\">Random Forest<\/a><\/strong> instead. They are more robust to noise.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"conclusion\">Conclusion<\/h2>\n\n\n\n<p>AdaBoost is a fundamental algorithm in the Machine Learning ecosystem.<\/p>\n\n\n\n<p>It introduced the world to the power of <strong>Boosting<\/strong>: the idea that many weak models can combine to become a master predictor.<\/p>\n\n\n\n<p>It works by:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>weighting errors,<\/li>\n\n\n\n<li>calculating performance (Alpha),<\/li>\n\n\n\n<li>and resampling data via buckets.<\/li>\n<\/ol>\n\n\n\n<p>If you have clean data and need a fast, accurate classifier, AdaBoost is an excellent choice.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"our-machine-learning-courses\">Our Machine Learning Courses<\/h2>\n\n\n\n<p>Explore our Machine Learning and&nbsp;<a href=\"https:\/\/www.mygreatlearning.com\/artificial-intelligence\/courses\" target=\"_blank\" rel=\"noreferrer noopener\">AI courses<\/a>, designed for comprehensive learning and skill development.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th><strong>Program Name<\/strong><\/th><th><strong>Duration<\/strong><\/th><\/tr><tr><th><a href=\"https:\/\/professionalonline2.mit.edu\/no-code-artificial-intelligence-machine-learning-program\">MIT No Code AI and Machine Learning Course<\/a><\/th><th>12 Weeks<\/th><\/tr><tr><th><a href=\"https:\/\/idss-gl.mit.edu\/mit-idss-data-science-machine-learning-online-program\">MIT Data Science and Machine Learning Course<\/a><\/th><th>12 Weeks<\/th><\/tr><tr><th><a href=\"https:\/\/www.mygreatlearning.com\/mit-data-science-and-machine-learning-program\">Data Science and Machine Learning Course<\/a><\/th><th>12 Weeks<\/th><\/tr><\/thead><\/table><\/figure>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn AdaBoost step by step. Step-by-step guide covering weak learners, weight updates, decision stumps, formulas, Python implementation, pros, and real use cases.<\/p>\n","protected":false},"author":41,"featured_media":113804,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[36799],"content_type":[],"class_list":["post-15100","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-machine-learning"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>AdaBoost: Adavptive Boosting Algorithm in Machine Learning<\/title>\n<meta name=\"description\" content=\"Learn AdaBoost step by step. Step-by-step guide covering weak learners, weight updates, decision stumps, formulas, Python implementation, pros, and real use cases.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.mygreatlearning.com\/blog\/adaboost-algorithm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Ultimate Guide to AdaBoost Algorithm\" \/>\n<meta property=\"og:description\" content=\"Learn AdaBoost step by step. 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