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010 |a  2013933452 
020 |a 9781461468486 
040 |a AR-OvUNE  |c AR-OvUNE 
042 |a lccopycat 
080 |a 519.87  |b K92 
100 1 |a Kuhn, Max. 
245 1 0 |a Applied predictive modeling /  |c Max Kuhn, Kjell Johnson. 
260 |a New York :  |b Springer,  |c c2013. 
300 |a xiii, 600 p. :  |b ill. (algunos col.) ;  |c 24 cm. 
504 |a Bibliografía: p.569-587 
505 0 |t 1. Introduction. --- I.General Strategies: 2. A Short Tour of the Predictive Modeling Process - 3.Data Pre-processing --- 4.Over-Fitting and Model Tuning --- II. Regression Models: 5.Measuring Performance in Regression Models --- 6.Linear Regression and Its Cousins --- 7.Nonlinear Regression Models --- 8.Regression Trees and Rule-Based Models --- 9.A Summary of Solubility Models --- 10.Case Study: Compressive Strength of Concrete Mixtures --- III. Classification Models: 11.Measuring Performance in Classification Models --- 12.Discriminant Analysis and Other Linear Classification Models --- 13.Nonlinear Classification Models --- 14.Classification Trees and Rule-Based Models --- 15.A Summary of Grant Application Models --- 16.Remedies for Severe Class Imbalance --- 17.Case Study: Job Scheduling --- IV. Other Considerations: 18.Measuring Predictor Importance --- 19.An Introduction to Feature Selection --- Factors That Can Affect Model Performance.--- Appendix. 
650 1 7 |a Estadística matemática  |2 Tesamat [en línea]  |9 11124 
650 1 7 |a Modelos matemáticos  |2 embne  |9 30679 
653 |a TEORIA DE LA PREDICCION 
653 |a MODELIZACION MATEMATICA 
700 1 |a Johnson, Kjell. 
856 |u http://appliedpredictivemodeling.com/  |y Sitio web del texto (se incluyen conjuntos de datos utilizados; figuras; software de programación R y fe de erratas entre otros elementos) 
942 |2 udc  |c LIB  |h 519.87  |i K92  |6 51987_K92