Extreme Gradient Boosting for Data Mining Applications

Name: Extreme Gradient Boosting for Data Mining Applications
Brand: LAP Lambert Academic Publishing
SKU: 9786138236122
Price: 172.91 RON
Availability: InStock

Autor: Nonita Sharma

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Editura: LAP Lambert Academic Publishing

Disponibilitate: În depozitul extern

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

Informații despre carte

Autor

Nonita Sharma

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2018

Pagini

EAN

9786138236122

Enbook ID

19346006

Editura

LAP Lambert Academic Publishing

Greutate

113

Dimensiuni

150 x 220 x 4

Descriere completă

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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