data_mining:xgboost

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data_mining:xgboost [2019/05/03 01:10]
phreazer
data_mining:xgboost [2019/05/03 01:13] (current)
phreazer [Taylor expansion]
Line 95: Line 95:
 $$\sum^n_{i=1} [l(y_i,​\hat{y}_i^{(t-1)}) + g_if_t(x_i) + \frac{1}{2}h_if_t^2(x_i)]$$ with $g_i=\delta_{\hat{y}^{(t-1)}} l(y_i,​\hat{y}^{(t-1)})$ and $h_i=\delta^2_{\hat{y}^{(t-1)}} l(y_i,​\hat{y}^{(t-1)})$ $$\sum^n_{i=1} [l(y_i,​\hat{y}_i^{(t-1)}) + g_if_t(x_i) + \frac{1}{2}h_if_t^2(x_i)]$$ with $g_i=\delta_{\hat{y}^{(t-1)}} l(y_i,​\hat{y}^{(t-1)})$ and $h_i=\delta^2_{\hat{y}^{(t-1)}} l(y_i,​\hat{y}^{(t-1)})$
  
-With removed constants+With removed constants ​(and square loss)
 $$\sum^n_{i=1} [g_if_t(x_i) + \frac{1}{2}h_if_t^2(x_i)] + \Omega(f_t)$$ ​ $$\sum^n_{i=1} [g_if_t(x_i) + \frac{1}{2}h_if_t^2(x_i)] + \Omega(f_t)$$ ​
 So that learning function only influences $g_i$ and $h_i$ while rest stays the same. So that learning function only influences $g_i$ and $h_i$ while rest stays the same.
  • data_mining/xgboost.txt
  • Last modified: 2019/05/03 01:13
  • by phreazer