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In general, the optimization procedure includes (i) which of the input variables are divided into regions, (ii) choice of the input variables and (iii) the value of the threshold. In short, we try to split the sub-samples even further at every iteration. We can continue until we have the same labels (for classification) in a sub-space or it is not possible to split it further. It means, there will be no error of classification in the model. However, such an effort would give complex trees, leading to poor generalization capabilities. In practice, simple trees are preferred over the complex structures, which is achieved by utilizing different tree inducer algorithms.
In general, the optimization procedure includes (i) which of the input variables are divided into regions, (ii) choice of the input variables and (iii) the value of the threshold. In short, we try to split the sub-samples even further at every iteration. We can continue until we have the same labels (for classification) in a sub-space or it is not possible to split it further. It means, there will be no error of classification in the model. However, such an effort would give complex trees, leading to poor generalization capabilities. In practice, simple trees are preferred over the complex structures, which is achieved by utilizing different tree inducer algorithms.
