<ul data-eligibleForWebStory="true"><li>Ensemble learning combines multiple models to create a robust and accurate model.</li><li>AdaBoost is a sequential ensemble method that combines multiple weak learners, often decision stumps.</li><li>AdaBoost focuses on learning from mistakes by giving higher importance to incorrectly classified instances.</li><li>Random Forest builds multiple full decision trees independently and in parallel.</li><li>Random Forest uses parallel voting to make predictions based on multiple decision trees.</li><li>In a fruit classification example, Random Forest uses the majority vote of decision trees to predict the fruit type.</li><li>Both AdaBoost and Random Forest have their strengths and are suitable for different scenarios.</li><li>Understanding the differences between AdaBoost and Random Forest helps in choosing the right ensemble method for real-world problems.</li></ul>

AdaBoost vs. Random Forest

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