At which point we have to stop post pruning in decision tree?

Post pruning is start from downward discarding subtree and include leaf node performance. so what is the best point or condition of the tree where we have to stop further pruning.

• Hi. You should really add more context to your questions. For example, it's not automatically clear what this "post pruning" procedure specifically does. You could describe it in the question or link to a website that does it for you. Otherwise, people probably won't know what exactly you're talking about. – Philip Raeisghasem Apr 6 '19 at 7:22
• Maybe this might help: saedsayad.com/decision_tree_overfitting.htm? – nbro Apr 6 '19 at 8:31
• post pruning is one of a method to avoid overfitting. it is use to reduce the complexity of tree. – hina munir Apr 6 '19 at 11:49
• @hinamunir How "post-pruning" different from "pre-pruning"? I had only heard of "pruning" in general. Your question is actually a useful question. Please, edit your post to include the definition of your terms, like post-pruning. – nbro Apr 9 '19 at 10:20

3. You can do what is done in Quinlan's 4.5 & C5.0 learners (which are the standard approaches; J48 is another implementation of the same algorithm). Quinlan performs a Chi-squared-like test for the relationship between the attribute we split upon and the target attribute. If the relationship is statistically significant, then the split is preserved. If not, it is not. The "confidence factor" parameter found in most implementations of these algorithms corresponds to the $$\alpha$$ value used in determining whether the relationship is considered significant. This approach captures the idea that we should prefer to keep branches that have few datapoints, but a very strong signal, or that have a weak signal, but very many datapoints supporting the pattern, since both cases are less likely to be overfitting that cases where we have weak signals and small numbers of points.