Fast Stepwise Stagewise Multivariateple Linear Regression (FSMLR) (previous name Fast Stepwise Multiple Linear Regression) is a procedure for stepwise stagewise building of linear regression models by means of greedy descriptor selection.
It can be viewed as a special case of the additive regression procedure (regression boosting) specially designed to be compatible with the three-set approach based on the use of three different sets for learning: training set, internal tuning validation set and external test set.
The main configurable parameters are: (1)
- shrinkage – its decrease leads to the decrease of generalization error and increase of the number of required iterations, and
...
- the relative size of the internal tuning validation set used for stopping descriptor selection procedure.
Reference
Zhokhova N, Baskin I, Palyulin V, Zefirov A, Zefirov N. Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies. Doklady Chemistry. 2007 Dec 1;417(2):282-284.