Decision Trees fragment the complex data into simpler forms. A Decision Tree classification tries to divide data until it can’t be further divided. A clear chart of all the possible contents is then created, which helps in further analysis. While a vast tree with numerous splices gives us a straight path, it can also generate a problem when testing the data. This excessive splicing leads to overfitting, wherein many divisions cause the tree to grow tremendously. In such cases, the predictive ability of the Decision Tree is compromised, and hence it becomes unsound. Pruning is a technique used to deal with overfitting, where the excessive subsets are removed.