Choque-Dextre, Yency E.

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    Modelo de clasificación y predicción en dos etapas: utilizando árboles de clasificación y el análisis de regresión multivariada
    (2015-06) Choque-Dextre, Yency E.; Acuña-Fernández, Edgar; College of Arts and Sciences - Sciences; Santana Morant, Dámaris; Lorenzo González, Edgardo; Department of Mathematics; Alers, Hilton
    Currently there exists a great variety of methods and algorithms attempting to optimize the process of classification. However, these methods do not take into account the internal structure of the classification datasets. For this reason, this research work has the goal of developing a classification model in two stages using classification and regression trees (CART) and the multivariate regression trees (MRT). Taking into account also the presence of missing values. This model has been applied to datasets from the National Agrarian University La Molina (Lima-Perú) whithin the Faculty of Economy and Planification of the Department of Statistics and Informatics, with the goal of predicting if a student who is admitted to the university will be able to complete the required curriculum in the alloted timeframe. To develop the proposed model, it was considered the academic performance of the students during their first year of university studies. Considering only those students with an optimum performance, it the missing values were estimated means of two statistical techniques: Multivariate And Regression Trees and the k-Nearest Neighbor Imputation. Then, it was elaborated a statistical model using the CART’s technique, and finally, to validate the proposed model, it was used the methodology of resubstitution and the technique of cross validation. According to our results the first stage can be done automatically using clustering if the academic program does not require many courses with high level of mathematics.