Items related to Extreme Gradient Boosting for Data Mining Applications
Synopsis
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, 2018
ISBN 10: 6138236122
ISBN 13: 9786138236122
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Condition: New. *FREE DOMESTIC SHIPPING until Monday, Oct. 19* 64 pp., paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Seller Inventory # ZB1315164
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
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Taschenbuch. Condition: 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. Seller Inventory # 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
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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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Taschenbuch. Condition: 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. Seller Inventory # 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 10: 6138236122
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Seller: moluna, Greven, Germany
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Condition: 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. Seller Inventory # 385846583
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Extreme Gradient Boosting for Data Mining Applications
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Seller Inventory # 9786138236122
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Extreme Gradient Boosting for Data Mining Applications
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ISBN 13: 9786138236122
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Taschenbuch. Condition: Neu. Extreme Gradient Boosting for Data Mining Applications | Nonita Sharma | Taschenbuch | 64 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786138236122 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 113474497
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