Recursive Partition in Biostatistics: Stability of Trees and Choice of the Most Stable Classification
Antonio Ciampi, Johanne Thiffault · COMPSTAT · 1988
Structures found in data by exploratory techniques are notoriously unstable. Suppose that we search for a model within a given family and that we do this on different samples from the same population, D 0 , D 1 ,..., D B . When only one data set is available, one can think of D as the original data set and the others as bootstrap samples from D 0 . Experience shows that one can be practically sure to find different models from different samples. A striking example of this model instability is given by Gong [1], in the context of stepwise logistic regression. The problem can be expected to be even more serious for tree-structured predictors, such as the RECPAM trees [2–4] which are the main concern of this work, since the model is selected out of a family much richer than that of linear regression as usually defined. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.