Generalized Feature Selection Analysis - Quick Tab

Select the Quick tab of the Generalized Feature Selection Analysis dialog box to access options to specify the variables for the analyses.

Variables

Click the Variables button to display the standard variables selection dialog box. Like in STATISTICA Data Mining, you can select continuous and categorical dependent variables, and continuous and categorical predictors. Continuous variables are those that are measured on a continuous scale (e.g., Height or Weight), categorical variables contain indicators, codes, or text values to denote membership in a group or class (e.g., Gender: Male, Female). Different statistics will be computed for categorical dependent variables (for classification) and continuous dependent variables (for regression-type problems); refer also to Feature Selection and Variable Screening Computational Details.

Counts variable

Click this button to display a standard variables selection dialog box where you can specify a variable with counts (such as in STATISTICA Data Mining). The values in this variable will be used during the computations as a simple case multiplier.

Criterion for selecting best predictors

Use the options in this group box to specify the criterion for selecting predictors and interactions between predictors for each dependent variable. See Feature Selection and Variable Screening Computational Details for a discussion of the criteria used for variable screening; see also the Feature Selection and Variable Screening Introductory Overview, in particular the section on Capitalizing on Chance, for additional details.

Display _ best predictors

Select this option button to display the best k predictors; for regression-type problems (for continuous dependent variables), the k predictors with the largest F values will be chosen, for classification-type problems the k predictors with the largest chi-square values will be chosen.

Display best predictors with p <

Select this option button to display the list of best predictors for which the p value is less than the value specified in the adjacent edit field. The list of predictors will be sorted in ascending order by p. For regression-type problems (for continuous dependent variables), the p will be computed from the respective F values, for classification-type problems p will be computed from the respective chi-square values. Choose this option when the predictor list in regression-type problems consists of continuous and categorical variables with different numbers of classes (groups), or when there are very few observations, in which case the ordering according to the p criterion can be quite different from that based on the F statistic.