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AdaBoost short for Adaptive Boosting is a machine learning meta-algorithm that works on the principle of Boosting. Gradient boosting is one of the most powerful techniques for building predictive models.

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Unlike a statistical ensemble in statistical mechanics which is usually infinite a machine learning ensemble consists of only a concrete finite set of alternative models but.

. A machine learning models performance is calculated by comparing its training accuracy with validation accuracy which is achieved by splitting the data into two sets. After reading this post you will know. In this post you will discover the gradient boosting machine learning algorithm and get a gentle introduction into where it came from and how it works.

The origin of boosting from learning theory and AdaBoost. The training set and validation set. Machine Learning Project Ideas.

In statistics and machine learning ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Bagging is used and the AdaBoost model implies the Boosting algorithm. Random forest is an ensemble learning algorithm that uses the concept of Bagging.

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