A methodology to find the best classifier in business decision
Сохранить в:
| Авторы: | , |
|---|---|
| Формат: | artículo original |
| Статус: | Versión publicada |
| Дата публикации: | 2015 |
| Описание: | 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. |
| Страна: | Portal de Revistas UCR |
| Институт: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Язык: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/19971 |
| Online-ссылка: | https://revistas.ucr.ac.cr/index.php/economicas/article/view/19971 |
| Ключевое слово: | SUPERVISED CLASSIFICATION CROSS VALIDATION ERROR RATE CUSTOMER STATISTICAL DECISION MULTIVARIATE ANALYSIS CLASIFICACIÓN SUPERVISADA VALIDACIÓN CRUZADA TASA DE ERROR CLIENTE DECISIÓN ESTADÍSTICA ANÁLISIS MULTIVARIABLE |