Keywords
CROSS VALIDATION
ERROR RATE
CUSTOMER
STATISTICAL DECISION
MULTIVARIATE ANALYSIS
CLASIFICACIÓN SUPERVISADA
VALIDACIÓN CRUZADA
TASA DE ERROR
CLIENTE
DECISIÓN ESTADÍSTICA
ANÁLISIS MULTIVARIABLE
How to Cite
Abstract
In this research, a methodology is presented to improve strategies of analysis in situations where supervised classification becomes the fundamental tool for business decision. The need to categorize the new customers into one of several groups, according to the characteristics of the subject, is analyzed through the calculation of the error rate. Programs were written using the statistical software package R, to calculate the error rate of each of nine classifiers, using cross-validation method 10 (Stone, 1974), in the 50 permutations of the data under consideration. For each of the analyzed data sets it was demonstrated, through ANOVA, that there are indeed significant differences in the average error rates of classifiers (p=0.00); therefore, it is concluded that the best classifier is the one with the lowest error rate.References
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