How to: |
RF_CLASSIFY creates a random forest, which is an ensemble of decision trees. Each decision tree produces an independent classification prediction, and the prediction of the forest is the majority vote of the individual predictions.
RF_CLASSIFY(options, number_of_trees, predictor_field1[, predictor_field2, ...] target_field)
where:
Reserved for future use.
Integer
Is the number of decision trees in the forest.
Numeric
Are one or more predictor field names.
Numeric
Is the target field.
The following procedure uses RF_CLASSIFY to predict education level, using a random forest with 100 decision trees, with predictors age, income, population range, and gender. The DEFINE FILE command creates virtual fields with correct numeric formats for use in the function.
DEFINE FILE WF_RETAIL POP_CODE/I2 = DECODE WF_RETAIL_GEOGRAPHY_CUSTOMER.CITY_POPULATION_RANGE ( 'H: 100,001 - 250,000' 1, 'I: 250,001 - 1,000,000' 2, 'J: 1,000,001 - 10,000,000' 3, 'K: 10,000,001 - 50,000,000' 4, ELSE 0 ); GENDER_CODE/I2 = DECODE WF_RETAIL_CUSTOMER.GENDER ( 'M' 1, 'F' 0 ); END
TABLE FILE WF_RETAIL
PRINT ID_CUSTOMER AGE INCOME DEGREE_M EDUC_LEVEL_M
COMPUTE ED_PRED/I2=RF_CLASSIFY(' ',100,
AGE,
INCOME,
POP_CODE,
GENDER_CODE,
EDUC_LEVEL_M);
WHERE POP_CODE NE 0;
WHERE OUTPUTLIMIT IS 12;
ON TABLE SET PAGE NOLEAD
ON TABLE SET STYLE *
GRID=OFF,$
ENDSTYLE
END
Partial output is shown in the following image.
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