The arboretum procedure
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- Example 3: Modeling a Binary Target with the DMINE Procedure Features
- Output DMINE Status Monitor
The NOMONITOR option suppresses the monitor that displays the execution status of the DMINE procedure. nomonitor; The DMINE Procedure Example 3: Modeling a Binary Target with the DMINE Procedure Features: Setting the MINR2= and STOPR2= cutoff values. q Specifying the target and input variables. q As a marketing analyst at a catalog company, you want to determine the inputs that best predict whether or not a customer will make a purchase from your new fall outerwear catalog. The fictitious catalog mailing data set is named SAMPSIO.DMEXA1 (stored in the sample library). The data set contains 1,966 customer cases. The binary target (PURCHASE) contains a formatted value of "Yes" if a purchase was made and a formatted value of "No" if a purchase was not made. There are 48 input variables available for predicting the target. Note that AMOUNT is an interval target that is modeled in Examples 1 and 2 of this chapter. ACCTNUM is an id variable, which is not a suitable input variable. Program
proc dmdb batch data=sampsio.dmexa1 out=dmbexa1 dmdbcat=catexa1; id acctnum; var amount income homeval frequent recency age domestic apparel leisure promo7 promo13 dpm12 county return mensware flatware homeacc lamps linens blankets towels outdoor coats wcoat wappar hhappar jewelry custdate numkids travtime job; class purchase(desc) marital ntitle gender telind aprtmnt snglmom mobile kitchen luxury dishes tmktord statecod race origin heat numcars edlevel; run;
proc dmine data=WORK.dmbexa1 dmdbcat=catexa1
minr2=0.020 stopr2=0.0050; var income homeval frequent recency age domestic apparel leisure promo7 promo13 dpm12 county return mensware flatware homeacc lamps linens blankets towels outdoor coats wcoat wappar hhappar jewelry custdate numkids travtime job marital ntitle gender telind aprtmnt snglmom mobile kitchen luxury dishes tmktord statecod race origin heat numcars edlevel;
target purchase; title 'DMINE: Binary Target'; run;
Output DMINE Status Monitor When you invoke the DMINE procedure, the Dmine Status Monitor window appears. This window monitors the execution time of the procedure.
This section of the output ranks all model effects by their R-square values. The degrees of freedom (DF) associated with each effect is also listed. The significant variables are analyzed in a subsequent forward stepwise regression. Non-significant variables are labeled as having an R2 value less than the MINR2 cutoff; these variables are not chosen in the final model. There are four types of model effects:
is calculated for each class effect using a one-way analysis of variance. Two-factor interaction effects are constructed by combining all possible levels of each class variable into one term. The degrees of freedom for a class effect is equal to: (the number of unique factor levels minus 1). For two-factor interactions, the degrees of freedom is equal to: (the number of levels in factor A multiplied by the number of levels in factor B minus 1). You can omit the two-factor interaction effects from the final stepwise analysis by specifying the NOINTER option on the PROC DMINE q
statement. Group effects are created by reducing each class effect through an analysis of means. The degrees of freedom for each group effect is equal to the number of levels. Since the USEGROUPS option was not specified in the PROC DMINE statement, the group effects and the original class effects can be used in the final model. q
VAR effects are estimated from interval variables as standard regression inputs. A simple linear regression is performed to determine the R2 statistic for interval inputs. The degrees of freedom is always equal to 1. q
AOV16 effects may account for possible non-linearity in the target variable PURCHASE. The degrees of freedom are calculated as the number of groups. The AOV16 variables can be expensive to compute. You can prevent these variables from being evaluated in the forward stepwise regression by specifying the NOAOV16 option in the PROC DMINE statement. q
For this example, the class KITCHEN*STATECOD interaction has the largest R2 with the target PURCHASE. Class and group interactions composed of the same terms have very similar R2 values. Of all the AOV16 variables, FREQUENT has the largest R2 value with the target.
DMINE: Binary Target R-Squares for Target variable: PURCHASE Effect DF R2 ------------------------------------------------------------ Class: KITCHEN*STATECOD 197 0.1045 Group: KITCHEN*STATECOD 8 0.1026 Class: NTITLE*STATECOD 191 0.0955 Group: NTITLE*STATECOD 9 0.0940 Class: STATECOD*ORIGIN 166 0.0917 Group: STATECOD*ORIGIN 8 0.0899 Class: DISHES*STATECOD 169 0.0875 Group: DISHES*STATECOD 8 0.0862 Class: STATECOD*EDLEVEL 146 0.0736 Group: STATECOD*EDLEVEL 9 0.0723 Class: STATECOD*HEAT 141 0.0643 Group: STATECOD*HEAT 9 0.0633 Class: LUXURY*STATECOD 101 0.0607 Class: STATECOD*NUMCARS 121 0.0597 Group: LUXURY*STATECOD 10 0.0597 AOV16: FREQUENT 11 0.0592 Group: STATECOD*NUMCARS 9 0.0585 Class: STATECOD*RACE 105 0.0575 Class: TMKTORD*STATECOD 110 0.0571 Group: STATECOD*RACE 9 0.0566 Group: TMKTORD*STATECOD 8 0.0562 Class: MARITAL*STATECOD 107 0.0537 Group: MARITAL*STATECOD 10 0.0526 Class: GENDER*STATECOD 104 0.0514 Group: GENDER*STATECOD 10 0.0505 Var: FREQUENT 1 0.0498
Additional Effects Are Not Listed Group: KITCHEN*EDLEVEL 7 0.0216 Class: KITCHEN*RACE 25 0.0214 Class: TELIND*KITCHEN 14 0.0213 Group: TELIND*KITCHEN 5 0.0211
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