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130219s2013 nyua f 001 0 eng d |
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|c 12255
|d 12255
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|a 2013933452
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|a 9781461468486
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|a AR-OvUNE
|c AR-OvUNE
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|a lccopycat
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|a 519.87
|b K92
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1 |
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|a Kuhn, Max.
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|a Applied predictive modeling /
|c Max Kuhn, Kjell Johnson.
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260 |
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|a New York :
|b Springer,
|c c2013.
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300 |
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|a xiii, 600 p. :
|b ill. (algunos col.) ;
|c 24 cm.
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504 |
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|a Bibliografía: p.569-587
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0 |
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|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.
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650 |
1 |
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|a Estadística matemática
|2 Tesamat [en línea]
|9 11124
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650 |
1 |
7 |
|a Modelos matemáticos
|2 embne
|9 30679
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653 |
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|a TEORIA DE LA PREDICCION
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653 |
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|a MODELIZACION MATEMATICA
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700 |
1 |
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|a Johnson, Kjell.
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856 |
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|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)
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942 |
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|2 udc
|c LIB
|h 519.87
|i K92
|6 51987_K92
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