XGBoost. The Extreme Gradient Boosting for Mining Applications

Name: XGBoost. The Extreme Gradient Boosting for Mining Applications
Brand: Grin Verlag
SKU: 9783668660618
Price: 3236.0 INR
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ISBN13: 9783668660618
Binding: Paperback
Subject: N/A
Publisher: Grin Verlag
Publisher Imprint: Grin Verlag
Publication Date: March 14, 2018
Pages: 60
Original Price: USD 38.5
Language: English
Edition: N/A
Item Weight: 91 grams
BISAC Subject(s): Internet / General and Data Science / Data Modeling & Design

About The Book

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 t

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