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Ensemble learning is basically the method of taking advantage of more than one model to get the combined prowess of the models to achieve better accuracy of analysis. Due to the complexity of the data that we aggregate from a pipeline system, we may require the usage of multiple classification models to come to the conclusion. While there are various ensemble learning models, we choose random forest classifiers. Random forest classifier is one where we break the actual dataset into various bootstrapped samples and bind them with particular features that we want to classify them on. Then we fit these datasets into trees considering selected features only. Then the result that we get from these trees is averaged to get the final result. A representative figure to explain is given in Figure 1.8.

Figure 1.8 Evaluation of random forest classifier for leakage detection.