Könyv Extreme Gradient Boosting for Data Mining Applications Nonita Sharma

Extreme Gradient Boosting for Data Mining Applications

Szerző: Nonita Sharma
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
12 082 Ft
Prediction models have reached to a stage where a single model is not sufficient to make predictions...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2018
oldal
64
EAN
9786138236122
Enbook ID
19346006
Súly
113
Méretek
150 x 220 x 4

Teljes leírás

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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