Nonita Sharma

Extreme Gradient Boosting for Data Mining Applications

Buch

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 func… Mehr

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Produktdetails

ISBN: 978-613-8-23612-2
EAN: 9786138236122
Produktnummer: 37462935
Verlag: LAP Lambert Academic Publishing
Sprache: Englisch
Erscheinungsjahr: 2018
Seitenangabe: 64 S.
Masse: H22.0 cm x B15.0 cm x D0.4 cm 113 g
Abbildungen: Paperback
Gewicht: 113

Über den Autor

Nonita 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 Sensor Networks, IoT, Big Data Analytics, and Data Mining.