Contents
What is a good decision tree accuracy?
Accuracy can be computed by comparing actual test set values and predicted values. Well, you got a classification rate of 67.53%, considered as good accuracy. You can improve this accuracy by tuning the parameters in the Decision Tree Algorithm.
How is validation data used to prune a decision tree?
Cross-validation is the process of building a tree with most of the data and then using the remaining part of the data to test the accuracy of the decision tree. Smallest tree. The tree is pruned back slightly further than the minimum error.
How does pruning reduce the size of a decision tree?
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting .
How to prevent / tell if decision tree is overfitting?
It is called Prunning. Beside general ML strategies to avoid overfitting, for decision trees you can follow pruning idea which is described (more theoretically) here and (more practically) here. In SciKit-Learn, you need to take care of parameters like depth of the tree or maximum number of leafs.
Why do you need to prune a Learning Tree?
Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. There are many techniques for tree pruning that differ in the measurement that is used to optimize performance.
What does it mean to prune a regression tree?
Pruning is a technique associated with classification and regression trees. I am not going to go into details here about what is meant by the best predictor variable, or a better partition. Instead I am going to discuss two enhancements to that basic outline: pruning and early stopping.