Why might a pruned decision tree that does not fit the data so well be better than an unpruned one?

The shortening of branches of the tree. Pruning is useful because classification trees may fit the training data well, but may do a poor job of classifying new values. A simpler tree often avoids over-fitting. As you can see, a pruned tree has less nodes and has less sparsity than a unpruned decision tree.Click to see full answer. In this regard, what is pruning of decision tree and why?Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.Likewise, what is overfitting in decision tree? Over-fitting is the phenomenon in which the learning system tightly fits the given training data so much that it would be inaccurate in predicting the outcomes of the untrained data. In decision trees, over-fitting occurs when the tree is designed so as to perfectly fit all samples in the training data set. Besides, what’s the difference between pre pruning and post pruning decision trees? Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.What is Overfitting and Underfitting?It occurs when the model or algorithm does not fit the data enough. Underfitting occurs if the model or algorithm shows low variance but high bias (to contrast the opposite, overfitting from high variance and low bias). It is often a result of an excessively simple model.

Leave a Reply

Your email address will not be published. Required fields are marked *