XGBoost: A Scalable Tree Boosting System: Difference between revisions
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(Created page with "== Presented by == *Qianying Zhao *Hui Huang *Lingyun Yi *Jiayue Zhang *Siao Chen *Rongrong Su *Gezhou Zhang *Meiyu Zhou == 2 Tree Boosting In A Nutshell == === 2.1 Regular...") |
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* Contains one score in each leaf value | * Contains one score in each leaf value | ||
[[File: | [[File:tree_model.PNG|left]] | ||
2. Model and Parameter | 2. Model and Parameter | ||
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So <math>\sum_{i=1}^n l(y_i,\hat y_i)+\sum^K_{k=1}\omega(f_k), f_k \in Ƒ</math> is the target function that needed to minimize. | So <math>\sum_{i=1}^n l(y_i,\hat y_i)+\sum^K_{k=1}\omega(f_k), f_k \in Ƒ</math> is the target function that needed to minimize. | ||
First looking at <math>\hat y_i</math> | First looking at <math>\hat y_i</math> | ||
Revision as of 23:48, 21 November 2018
Presented by
- Qianying Zhao
- Hui Huang
- Lingyun Yi
- Jiayue Zhang
- Siao Chen
- Rongrong Su
- Gezhou Zhang
- Meiyu Zhou
2 Tree Boosting In A Nutshell
2.1 Regularized Learning Objective
1. Regression Decision Tree (also known as classification and regression tree):
- Decision rules are the same as in decision tree
- Contains one score in each leaf value
2. Model and Parameter
Model: Assuming there are K trees
[math]\displaystyle{ \hat \y_i = \sum^K_{k=1} f_k(x_I), f_k \in Ƒ }[/math]
Objective: [math]\displaystyle{ Obj = \sum_{i=1}^n l(y_i,\hat y_i)+\sum^K_{k=1}\omega(f_k) }[/math]
where [math]\displaystyle{ \sum^n_{i=1}l(y_i,\hat y_i) }[/math] is training loss, [math]\displaystyle{ \sum_{k=1}^K \omega(f_k) }[/math] is complexity of Trees
So [math]\displaystyle{ \sum_{i=1}^n l(y_i,\hat y_i)+\sum^K_{k=1}\omega(f_k), f_k \in Ƒ }[/math] is the target function that needed to minimize.
First looking at [math]\displaystyle{ \hat y_i }[/math]