Feature & Model Selection; Regression

Double-click Feature and method selection: Regression on the Feature & Method Selection Startup Panel - Quick tab to display the Feature & Model Selection; Regression dialog box. You also can select Feature and method selection: Regression and click the OK button to display this dialog box, which contains two tabs: Quick and Advanced.

In addition to feature selection methods, based on those described in detail in the documentation of the Feature Selection and Variable Screening module, Statistica Process Optimization can also simultaneously evaluate the importance of predictors and the particular types or classes of models that are likely to identify the respective predictors. Specifically, the program can automatically fit linear models [stepwise regression or stepwise linear discriminant function analysis; see General Regression Models (GRM) and General Discriminant Analysis (GDA)], classification and regression trees [see General Classification and Regression Trees (GC&RT)], Multivariate Adaptive Regression Splines (MARSplines), boosted tree models (see Boosted Trees; this is an implementation of stochastic gradient boosting), and various neural network architectures. Next the program will rank the importance of each predictor in each type of model, and report those rankings as well as the average importance ranking over all models. Optionally, Process Optimization can also report a ranking of models based on simple summary statistics (R-square, misclassification rate) for each model. In summary, these options enable you to evaluate the importance of various predictors in different types of Statistical or machine learning models, as well as the type or class of model that is best suited for the analyses of those predictors. In short, the program will identify the important predictors and the best method to use in order to identify those predictors.