Inference
Thus Decision Trees are very useful algorithms as they are not only used to choose alternatives based on expected values but are also used for the classification of priorities and making predictions. It is up to us to determine the accuracy of using such models in the appropriate applications.
Decision Tree in R Programming
Decision Trees are useful supervised Machine learning algorithms that have the ability to perform both regression and classification tasks. It is characterized by nodes and branches, where the tests on each attribute are represented at the nodes, the outcome of this procedure is represented at the branches and the class labels are represented at the leaf nodes. Hence it uses a tree-like model based on various decisions that are used to compute their probable outcomes. These types of tree-based algorithms are one of the most widely used algorithms due to the fact that these algorithms are easy to interpret and use. Apart from this, the predictive models developed by this algorithm are found to have good stability and a descent accuracy due to which they are very popular