As briefly mentioned in the preceding section, different classifiers will be applied on the same training data and the results ensembled either taking majority voting or applying another classifier (also known as a meta-classifier) fitted on results obtained from individual classifiers. This means, for meta-classifier X, variables would be model outputs and Y variable would be an actual 0/1 result. By doing this, we will obtain the weightage that should be given for each classifier and those weights will be applied accordingly to classify unseen observations. All three methods of application of ensemble of ensembles are shown here:
- Majority voting or average: In this method, a simple mode function (classification problem) is applied to select the category with the major number of appearances out of individual classifiers. Whereas, for regression problems, an average will be calculated to compare against actual values.
- Method of application...