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02911cam a2200625 4500 |
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PPN181556634 |
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http://www.sudoc.fr/181556634 |
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20240214061000.0 |
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|a 978-1-461-46848-6
|b rel.
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|a 1-461-46848-5
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|a (OCoLC)894324208
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|a ocn827083441
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|a AU@000051644343
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|a eng
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|a US
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|a a a 001yy
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|a r
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|6 z01
|c txt
|2 rdacontent
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|b xxxe##
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|6 z01
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|a Applied predictive modeling
|f Max Kuhn, Kjell Johnson
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|a [Édition corrigée au 5e tirage en 2016]
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|a New York
|c Springer
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|d C 2013
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|a 1 vol. (XIII-600 p.)
|c ill., couv. ill. en coul.
|d 24 cm
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|a Autre tirage : 2016
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|a Bibliogr. p. [569]-587. Index
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|b Part I. General Strategies
|c A Short Tour of the Predictive Modeling Process
|c Data Pre-processing
|c Over-Fitting and Model Tuning
|b Part II. Regression Models
|c Measuring Performance in Regression Models
|c Linear Regression and Its Cousins
|c Nonlinear Regression Models
|c Regression Trees and Rule-Based Models
|c A Summary of Solubility Models
|c Case Study: Compressive Strength of Concrete Mixtures
|b Part III. Classification Models
|c Measuring Performance in Classification Models
|c Discriminant Analysis and Other Linear Classification Models
|c Nonlinear Classification Models
|c Classification Trees and Rule-Based Models
|c A Summary of Grant Application Models
|c Remedies for Severe Class Imbalance
|c Case Study: Job Scheduling
|b Part IV. Other Considerations
|c Measuring Predictor Importance
|c An Introduction to Feature Selection
|c Factors That Can Affect Model Performance
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452 |
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|0 170487997
|t Applied Predictive Modeling
|f by Max Kuhn, Kjell Johnson.
|e 1st ed. 2013.
|c New York, NY
|n Springer New York
|d 2013
|y 978-1-461-46849-3
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|3 PPN027296539
|a Statistique mathématique
|2 rameau
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|3 PPN027551385
|a Modèles mathématiques
|2 rameau
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|3 PPN027701514
|a Théorie de la prévision
|2 rameau
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|3 PPN143195344
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|2 fmesh
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|3 PPN04080299X
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|3 PPN04074194X
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|3 PPN040708527
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|a 519.5
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