![]() ![]() Random forest is an example of the bagging method of ensemble models, and we will use caret in R to demonstrate it. It's worth reading the Kaggle ensembling guide to know more about the winning strategies for developing the ensemble models. ![]() Similarly, there are various other approaches such as weighing and rank averaging. Majority voting is a process in which the class with the largest number of predictions in all of the classifiers becomes the prediction of the ensemble. Finally, the results are combined by averaging the results or selecting the best prediction using majority voting. Models are built on each sample using supervised learning methods. In this method, random samples are prepared from training data set using the bootstrapping process (random sample with replacement models). Random forest reduces this instability by running multiple instances which leads to lower variance. Sometimes the weak learning algorithms are unstable - a slightly different input leads to very different outputs. ![]() BaggingĪlso known as bootstrap aggregation, it is the way to decrease the variance error of a model’s result. Mostly there are three types of ensemble learning methods that are generally used as described below. The result has better predictive performance than the results of any of its constituent learning algorithms separately. Technically, ensemble models are composed of several supervised learning models that are independently trained, and the results are combined in different ways to obtain the final prediction result. Likewise, in the Machine Learning world, ensemble models is a "team of models" working together to improve the result of their work. It is a well-thought approach very closely related to a power-packed word - TEAM !! Any work done by a team leads to significant achievements. Ensembling models is a robust approach to improving the accuracy of the predictive models. ![]()
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