data_mining:xgboost

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data_mining:xgboost [2019/05/02 23:13] – [Taylor expansion] phreazerdata_mining:xgboost [2020/08/02 14:12] (current) – phreazer
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 ===== Gradient boosting ===== ===== Gradient boosting =====
  
-$F$ is space of functions containing all regression trees +  * $F$ is space of functions containing all regression trees 
-$K$ is number of trees +  * $K$ is number of trees 
-$f_k(x_i)$ is regression tree that maps a attribute to a score+  * $f_k(x_i)$ is regression tree that maps a attribute to a score
  
 Learn functions (trees) instead of weights in $R^d$. Learn functions (trees) instead of weights in $R^d$.
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 Learning objective: Learning objective:
-  * Training loss: Fit of the functions to the points +  * **Training loss**: Fit of the functions to the points 
-  * Regularization: Complexity of function; Number of splitting points, l2 norm of height in each segment+  * **Regularization**: Complexity of function; Number of splitting points, l2 norm of height in each segment
  
 Objective: Objective:
  • data_mining/xgboost.1556838839.txt.gz
  • Last modified: 2019/05/02 23:13
  • by phreazer