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Feature Engineering and Selection : a Practical Approach for Predictive Models.

Author: Max Kuhn; Kjell Johnson
Publisher: Milton : CRC Press LLC, 2019.
Series: Chapman and Hall/CRC Data Science series.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Kuhn, Max.
Feature Engineering and Selection : A Practical Approach for Predictive Models.
Milton : CRC Press LLC, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Max Kuhn; Kjell Johnson
ISBN: 9781351609470 1351609475 9781315108230 1315108232 9781351609456 1351609459 9781351609463 1351609467
OCLC Number: 1111432732
Description: 1 online resource (314 pages)
Contents: 1. IntroductionA Simple ExampleImportant ConceptsA More Complex ExampleFeature SelectionAn Outline of the BookComputing2. Illustrative Example: Predicting Risk of Ischemic StrokeSplittingPreprocessingExplorationPredictive Modeling Across SetsOther ConsiderationsComputing3. A Review of the Predictive Modeling ProcessIllustrative Example: OkCupid Profile DataMeasuring PerformanceData SplittingResamplingTuning Parameters and OverfittingModel Optimization and TuningComparing Models Using the Training SetFeature Engineering Without OverfittingSummaryComputing4. Exploratory VisualizationsIntroduction to the Chicago Train Ridership DataVisualizations for Numeric Data: Exploring Train Ridership DataVisualizations for Categorical Data: Exploring the OkCupid DataPost Modeling Exploratory VisualizationsSummaryComputing5. Encoding Categorical PredictorsCreating Dummy Variables for Unordered CategoriesEncoding Predictors with Many CategoriesApproaches for Novel CategoriesSupervised Encoding MethodsEncodings for Ordered DataCreating Features from Text DataFactors versus Dummy Variables in Tree-Based ModelsSummaryComputing6. Engineering Numeric PredictorsTransformationsMany TransformationsMany: Many TransformationsSummaryComputing7. Detecting Interaction EffectsGuiding Principles in the Search for InteractionsPractical ConsiderationsThe Brute-Force Approach to Identifying Predictive InteractionsApproaches when Complete Enumeration is Practically ImpossibleOther Potentially Useful ToolsSummaryComputing8. Handling Missing DataUnderstanding the Nature and Severity of Missing InformationModels that are Resistant to Missing ValuesDeletion of DataEncoding MissingnessImputation methodsSpecial CasesSummaryComputing9. Working with Profile DataIllustrative Data: Pharmaceutical Manufacturing MonitoringWhat are the Experimental Unit and the Unit of Prediction?Reducing BackgroundReducing Other NoiseExploiting CorrelationImpacts of Data Processing on ModelingSummaryComputing10. Feature Selection OverviewGoals of Feature SelectionClasses of Feature Selection MethodologiesEffect of Irrelevant FeaturesOverfitting to Predictors and External ValidationA Case StudyNext StepsComputing11. Greedy Search MethodsIllustrative Data: Predicting Parkinson's DiseaseSimple FiltersRecursive Feature EliminationStepwise SelectionSummaryComputing12. Global Search MethodsNaive Bayes ModelsSimulated AnnealingGenetic AlgorithmsTest Set ResultsSummaryComputing
Series Title: Chapman and Hall/CRC Data Science series.

Abstract:

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

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"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this-as Read more...

 
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