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(Tree Boosting)
(Tree Boosting)
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=== Regularized Learning Objective ===
 
=== Regularized Learning Objective ===
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Suppose the given data set has m features and n examples. A tree ensemble model uses K additive functions to predict the output.
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[[File:File.png|200px|thumb|left|eq1]]
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where F is the space of regression trees(CART).
  
 
= Split Finding Algorithms =
 
= Split Finding Algorithms =

Revision as of 12:51, 16 March 2018

XGBoost: A Scalable Tree Boosting System

Presented by

Jiang, Cong

Song, Ziwei

Ye, Zhaoshan

Zhang, Wenling

Introduction

In recent years, XGBoost has been the most popular tool in Kaggle, as well as other machine learning and data mining challenges. The main reason to its success is that this tree boosting system is highly scalable , which means it could be used to solve a variety of different problems.

This paper is written by XGBoost's father, Tianqi Chen. It gives us an overview on how he used algorithmic optimizations as well as some important systems to develop XGBoost. He explained it in the following manner:

1. Based on gradient tree boosting algorithm as well as using shrinkage and feature subsampling to prevent overfitting, Chen introduced an end-to-end tree boosting system.

2.

Tree Boosting

Regularized Learning Objective

Suppose the given data set has m features and n examples. A tree ensemble model uses K additive functions to predict the output.

where F is the space of regression trees(CART).

Split Finding Algorithms

System Design

Related Works

End to End Evaluations

Results

Conclusion

Criticisms

Source

**Sample format

Recurrent neural networks are a variation of deep neural networks that are capable of storing information about previous hidden states in special memory layers.<ref name=lstm> Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780. </ref> Unlike feed forward neural networks that take in a single fixed length vector input and output a fixed length vector output, recurrent neural networks can take in a sequence of fixed length vectors as input, because of their ability to store information and maintain a connection between inputs through this memory layer. By comparison, previous inputs would have no impact on current output for feed forward neural networks, whereas they can impact current input in a recurrent neural network. (This paper used the LSTM formulation from Graves<ref name=grave> Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013). </ref>)


Where [math]\,S[/math] is the base/source sentence, [math]\,T[/math] is the paired translated sentence and [math]\,T_r[/math] is the total training set. This objective function is to maximize the log probability of a correct translation [math]\,T[/math] given the base/source sentence [math]\,S[/math] over the entire training set. Once the training is complete, translations are produced by finding the most likely translation according to LSTM:

[math]\hat{T} = \underset{T}{\operatorname{arg\ max}}\ p(T|S)[/math]


It has been showed that Long Short-Term Memory recurrent neural networks have the ability to generate both discrete and real-valued sequences with complex, long-range structure using next-step prediction <ref name=grave> Reference </ref>.