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Synopsis
Marketing research is often criticized for lacking generalizability and inability to reproduce results. The problem lies in using models to fit data, rather than determining the predictive power of models in conditions of uncertainty. For instance, how does the predictive power of a model change when customer dynamics change? The current study suggests that marketing researchers can supplement existing research methods with non-probabilistic prediction methods, such as the kNN algorithm-based model. Unlike probabilistic models that rely on past outcomes to predict future events – and lose predictive power when newer events are observed - non-probabilistic models better capture uncertainty. In the current study, the predictive power of the kNN algorithm-based model and the Naļve Bayes model is compared using data from two real markets. The kNN algorithm-based model provides more accurate predictions, showing the utility of combining the kNN algorithm-based model with existing marketing research to improve the predictability and generalizability of models. Implications for research and future research are discussed.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2022
- ISBN 10 6204980637
- ISBN 13 9786204980638
- BindingPaperback
- LanguageEnglish
- Number of pages56
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Increasing Generalizability: Na?ve Bayes Vs K-Nearest Neighbors
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
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Increasing Generalizability: Naļve Bayes Vs K-Nearest Neighbors
Published by
LAP LAMBERT Academic Publishing Jun 2022, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Marketing research is often criticized for lacking generalizability and inability to reproduce results. The problem lies in using models to fit data, rather than determining the predictive power of models in conditions of uncertainty. For instance, how does the predictive power of a model change when customer dynamics change The current study suggests that marketing researchers can supplement existing research methods with non-probabilistic prediction methods, such as the kNN algorithm-based model. Unlike probabilistic models that rely on past outcomes to predict future events - and lose predictive power when newer events are observed - non-probabilistic models better capture uncertainty. In the current study, the predictive power of the kNN algorithm-based model and the Naļve Bayes model is compared using data from two real markets. The kNN algorithm-based model provides more accurate predictions, showing the utility of combining the kNN algorithm-based model with existing marketing research to improve the predictability and generalizability of models. Implications for research and future research are discussed. 56 pp. Englisch. Seller Inventory # 9786204980638
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Increasing Generalizability: Na?ve Bayes Vs K-Nearest Neighbors
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
New
Softcover
Seller: Books Puddle, New York, NY, U.S.A.
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Increasing Generalizability: Naļve Bayes Vs K-Nearest Neighbors
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
New
Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Marketing research is often criticized for lacking generalizability and inability to reproduce results. The problem lies in using models to fit data, rather than determining the predictive power of models in conditions of uncertainty. For instance, how does the predictive power of a model change when customer dynamics change The current study suggests that marketing researchers can supplement existing research methods with non-probabilistic prediction methods, such as the kNN algorithm-based model. Unlike probabilistic models that rely on past outcomes to predict future events - and lose predictive power when newer events are observed - non-probabilistic models better capture uncertainty. In the current study, the predictive power of the kNN algorithm-based model and the Naļve Bayes model is compared using data from two real markets. The kNN algorithm-based model provides more accurate predictions, showing the utility of combining the kNN algorithm-based model with existing marketing research to improve the predictability and generalizability of models. Implications for research and future research are discussed. Seller Inventory # 9786204980638
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Increasing Generalizability: Naļve Bayes Vs K-Nearest Neighbors
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Marketing research is often criticized for lacking generalizability and inability to reproduce results. The problem lies in using models to fit data, rather than determining the predictive power of models in conditions of uncertainty. For instance, how does. Seller Inventory # 651788572
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Increasing Generalizability: Na?ve Bayes Vs K-Nearest Neighbors
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
New
Softcover
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Increasing Generalizability: Naļve Bayes Vs K-Nearest Neighbors
Published by
LAP LAMBERT Academic Publishing Jun 2022, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -Marketing research is often criticized for lacking generalizability and inability to reproduce results. The problem lies in using models to fit data, rather than determining the predictive power of models in conditions of uncertainty. For instance, how does the predictive power of a model change when customer dynamics change The current study suggests that marketing researchers can supplement existing research methods with non-probabilistic prediction methods, such as the kNN algorithm-based model. Unlike probabilistic models that rely on past outcomes to predict future events æ and lose predictive power when newer events are observed - non-probabilistic models better capture uncertainty. In the current study, the predictive power of the kNN algorithm-based model and the Naļve Bayes model is compared using data from two real markets. The kNN algorithm-based model provides more accurate predictions, showing the utility of combining the kNN algorithm-based model with existing marketing research to improve the predictability and generalizability of models. Implications for research and future research are discussed.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch. Seller Inventory # 9786204980638
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