Fr. 37.90

XGBoost. The Extreme Gradient Boosting for Mining Applications

English · Paperback / Softback

Shipping usually within 1 to 2 weeks (title will be printed to order)

Description

Read more

Technical Report from the year 2017 in the subject Computer Science - Internet, New Technologies, grade: 8, , language: English, abstract: Tree boosting has empirically proven to be a highly effective and versatile approach for data-driven modelling. The core argument is that tree boosting can adaptively determine the local neighbourhoods of the model thereby taking the bias-variance trade-off into consideration during model fitting. Recently, a tree boosting method known as XGBoost has gained popularity by providing higher accuracy. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade-off even more carefully. In this research work, we propose to demonstrate the use of an adaptive procedure i.e. Learned Loss (LL) to update the loss function as the boosting proceeds. Accuracy of the proposed algorithm i.e. XGBoost with Learned Loss boosting function is evaluated using test/train method, K-fold cross validation, and Stratified cross validation method and compared with the state of the art algorithms viz. XGBoost, AdaBoost, AdaBoost-NN, Linear Regression(LR),Neural Network(NN), Decision Tree(DT), Support Vector Machine(SVM), bagging-DT, bagging-NN and Random Forest algorithms. The parameters evaluated are accuracy, Type 1 error and Type 2 error (in Percentages). This study uses total ten years of historical data from Jan 2007 to Aug 2017 of two stock market indices CNX Nifty and S&P BSE Sensex which are highly voluminous.Further, in this research work, we will investigate how XGBoost differs from the more traditional ensemble techniques. Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models.In addition to this, we will attempt to answer the question of why XGBoost seems to win so many competitions. To do this, we will provide some arguments for why tree boosting, and in particular XGBoost, seems to be such a highly effective and versatile approach to predictive modelling. The core argument is that tree boosting can be seen to adaptively determine the local neighbourhoods of the model. Tree boosting can thus be seen to take the bias-variance trade off into consideration during model fitting. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade off even more carefully.

Product details

Authors Nonita Sharma
Publisher Grin Verlag
 
Languages English
Product format Paperback / Softback
Released 01.01.2018
 
EAN 9783668660618
ISBN 978-3-668-66061-8
No. of pages 60
Dimensions 148 mm x 210 mm x 4 mm
Weight 101 g
Subjects Natural sciences, medicine, IT, technology > IT, data processing > Data communication, networks
Natural sciences, medicine, IT, technology > IT, data processing > Internet

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.