What a fitted tree can be trusted to mean
Section of Classification trees, part of Statistical learning
What a fitted tree can be trusted to mean
| Type | Section |
| Taught in | Classification trees |
| Part | Statistical learning |
Read a fitted tree from root to leaf to understand its decisions. Then ask whether those questions persist when the training sample changes. A small difference in the best first split can change the data reaching every later node. Pruning reduces complexity. It does not prove stability. Averaging many trees, each grown on a perturbed sample, is the next page’s answer to that sensitivity.
Used in
See also
Node impurity · Building a classification tree · Choosing a split · Invariance to a change of units · Multi-way splits and missing values · Tree size and pruning · Laboratory: prune using training cross-validation · Decision tree