Quick descriptive statistics of the data.
1 Answer1. Active Oldest Votes. 1. Pruning might lower the accuracy of the training set, since the tree will not learn the optimal parameters as well for the training set. However, if we do not overcome overfitting by setting the appropriate parameters, we might end up building a. When ccp_alpha is set to zero and keeping the other default parameters of DecisionTreeClassifier, the tree overfits, leading to a % training accuracy and 88% testing accuracy.
As alpha increases, more of the tree is pruned, thus creating a decision tree that generalizes better. In this example, setting ccp_alpha= maximizes the testing accuracy. Apr 13, 1. Pruning is supposed to improve classification by preventing overfitting. Since pruning will only occur if it improves classification rates on the validation set, a pruned tree will perform as well or better than an un-pruned tree during validation.
Outdated Answers: results from flagging exercise and next steps.
edited Oct. Jun 14, Pruning also simplifies a decision tree by removing the weakest rules. Pruning is often distinguished into: Pre-pruning (early stopping) stops the tree before it has completed classifying the training set, Post-pruning allows the tree to classify the training set perfectly and then prunes the tree. We will focus on post-pruning in this bushleaning.bar: Edward Krueger.