Extreme Gradient Boosting for Data Mining Applications

Name: Extreme Gradient Boosting for Data Mining Applications
Brand: LAP Lambert Academic Publishing
SKU: 9786138236122
Price: 710 CZK
Availability: InStock

Autor: Nonita Sharma

Jazyk:

Vazba: Brožovaná

Vydavatel: LAP Lambert Academic Publishing

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

710 Kč

Prediction models have reached to a stage where a single model is not sufficient to make predictions...

Informace o knize

Autor

Nonita Sharma

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2018

Stránek

EAN

9786138236122

Enbook ID

19346006

Vydavatel

LAP Lambert Academic Publishing

Hmotnost

113

Rozměry

150 x 220 x 4

Kompletní popis

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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Kompletní popis

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