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AdaBoost Algorithm
AdaBoost Algorithm Table Of Contents: Introduction What Is the AdaBoost Algorithm? Understanding the Working of the AdaBoost Algorithm (1) Introduction Boosting is a machine learning ensemble technique that combines multiple weak learners to create a strong learner. The term “boosting” refers to the idea of boosting the performance of weak models by iteratively training them on different subsets of the data. The main steps involved in a boosting algorithm are as follows: Initialize the ensemble: Initially, each instance in the training data is given equal weight, and a weak learner (e.g., a decision tree) is trained on the data. Iteratively
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Boosting Algorithms
Boosting Algorithms Table Of Contents: What Is Boosting In Machine Learning? Types Of Boosting Algorithm. (1) What Is Boosting? Boosting is a machine learning ensemble technique that combines multiple weak learners (typically decision trees) to create a strong learner. The main idea behind boosting algorithms is to iteratively train weak models in a sequential manner, where each subsequent model focuses on correcting the mistakes made by previous models. This iterative process gradually improves the overall predictive performance of the ensemble. (2) Types Of Boosting Algorithms. AdaBoost (Adaptive Boosting): AdaBoost assigns weights to each training instance and adjusts them based on
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Under Fitting Vs Over Fitting
Underfitting vs Overfitting Table Of Contents: What is Generalization What is Underfitting What is Overfitting How To Detect Underfitting How To Avoid Underfitting How To Detect Overfitting How To Prevent Overfitting Model Prone To Underfitting (1) What Is Generalization? In supervised learning, the main goal is to use training data to build a model that will be able to make accurate predictions based on new, unseen data, which has the same characteristics as the initial training set. This is known as generalization. Generalization relates to how effectively the concepts learned by a machine learning model apply to particular examples that were
