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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 Naïve 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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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: Na?ve Bayes Vs K-Nearest Neighbors
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
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Increasing Generalizability: Naïve Bayes Vs K-Nearest Neighbors
Verlag:
LAP LAMBERT Academic Publishing Jun 2022, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: 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 Naïve 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. Bestandsnummer des Verkäufers 9786204980638
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Increasing Generalizability: Na?ve Bayes Vs K-Nearest Neighbors
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
Neu
Softcover
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Increasing Generalizability: Naïve Bayes Vs K-Nearest Neighbors
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ISBN 10: 6204980637
ISBN 13: 9786204980638
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Zustand: 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. Bestandsnummer des Verkäufers 651788572
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Increasing Generalizability: Naïve Bayes Vs K-Nearest Neighbors
Verlag:
LAP LAMBERT Academic Publishing Jun 2022, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: 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 ¿ 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 Naïve 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. Bestandsnummer des Verkäufers 9786204980638
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Increasing Generalizability: Naïve Bayes Vs K-Nearest Neighbors
Verlag:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204980637
ISBN 13: 9786204980638
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: 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 Naïve 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. Bestandsnummer des Verkäufers 9786204980638
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