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Sinopsis
Prediction models have reached to a stage where a single model is not sufficient to make predictions. Hence, to achieve better accuracy and performance, an ensemble of various models are being used. Gradient Boosting Algorithm has almost been the part of all ensembles. Winners of Kaggle Competition are swearing by this. Extreme Gradient Boosting is a step forward to this where we try to optimise the loss function. In this research work Squared Logistic Loss function is used with Boosting function which is expected to reduce bias and variance. The proposed model is applied on stock market data for the past ten years. Squared Logistic Loss function with XGBoost promises to be an effective approach in terms of accuracy and better prediction.
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Prediction models have reached to a stage where a single model is not sufficient to make predictions. Hence, to achieve better accuracy and performance, an ensemble of various models are being used. Gradient Boosting Algorithm has almost been the part of all ensembles. Winners of Kaggle Competition are swearing by this. Extreme Gradient Boosting is a step forward to this where we try to optimise the loss function. In this research work Squared Logistic Loss function is used with Boosting function which is expected to reduce bias and variance. The proposed model is applied on stock market data for the past ten years. Squared Logistic Loss function with XGBoost promises to be an effective approach in terms of accuracy and better prediction.
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Extreme Gradient Boosting for Data Mining Applications
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LAP LAMBERT Academic Publishing Mrz 2018, 2018
ISBN 10: 6138236122
ISBN 13: 9786138236122
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Prediction models have reached to a stage where a single model is not sufficient to make predictions. Hence, to achieve better accuracy and performance, an ensemble of various models are being used. Gradient Boosting Algorithm has almost been the part of all ensembles. Winners of Kaggle Competition are swearing by this. Extreme Gradient Boosting is a step forward to this where we try to optimise the loss function. In this research work Squared Logistic Loss function is used with Boosting function which is expected to reduce bias and variance. The proposed model is applied on stock market data for the past ten years. Squared Logistic Loss function with XGBoost promises to be an effective approach in terms of accuracy and better prediction. 64 pp. Englisch. Nº de ref. del artículo: 9786138236122
Cantidad disponible: 2 disponibles
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Extreme Gradient Boosting for Data Mining Applications
Publicado por
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6138236122
ISBN 13: 9786138236122
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Prediction models have reached to a stage where a single model is not sufficient to make predictions. Hence, to achieve better accuracy and performance, an ensemble of various models are being used. Gradient Boosting Algorithm has almost been the part of all ensembles. Winners of Kaggle Competition are swearing by this. Extreme Gradient Boosting is a step forward to this where we try to optimise the loss function. In this research work Squared Logistic Loss function is used with Boosting function which is expected to reduce bias and variance. The proposed model is applied on stock market data for the past ten years. Squared Logistic Loss function with XGBoost promises to be an effective approach in terms of accuracy and better prediction. Nº de ref. del artículo: 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
Publicado por
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6138236122
ISBN 13: 9786138236122
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma NonitaNonita Sharma is currently working as an Assistant Professor in the Department of Computer Science & Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar. Her research interests include Wireless Sen. Nº de ref. del artículo: 385846583
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
ISBN 13: 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 13: 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 13: 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
ISBN 13: 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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LAP LAMBERT Academic Publishing Mär 2018, 2018
ISBN 10: 6138236122
ISBN 13: 9786138236122
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. Neuware -Prediction models have reached to a stage where a single model is not sufficient to make predictions. Hence, to achieve better accuracy and performance, an ensemble of various models are being used. Gradient Boosting Algorithm has almost been the part of all ensembles. Winners of Kaggle Competition are swearing by this. Extreme Gradient Boosting is a step forward to this where we try to optimise the loss function. In this research work Squared Logistic Loss function is used with Boosting function which is expected to reduce bias and variance. The proposed model is applied on stock market data for the past ten years. Squared Logistic Loss function with XGBoost promises to be an effective approach in terms of accuracy and better prediction.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Nº de ref. del artículo: 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6138236122
ISBN 13: 9786138236122
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