Contents
What are the parameters in XGBoost?
Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters.
- General parameters relate to which booster we are using to do boosting, commonly tree or linear model.
- Booster parameters depend on which booster you have chosen.
What is Reg lambda in XGBoost?
lambda: This is responsible for L2 regularization on leaf weights. alpha: This is responsible for L1 regularization on leaf weights. max_depth: It is a positive integer value, and is responsible for how deep each tree will grow during any boosting round.
How long does XGBoost take to train?
It took 30 mins to train model with no parameter tuning. If I run GridSearchCV to train model with 3 folds and 6 learning rate values, it will take more than 10 hours to return.
Which is the parameter tuning function in XGBoost?
XGBoost has a very useful function called as “cv” which performs cross-validation at each boosting iteration and thus returns the optimum number of trees required. Tune tree-specific parameters (max_depth, min_child_weight, gamma, subsample, colsample_bytree) for decided learning rate and number of trees.
When do you tune gamma in XGBoost cross?
In the rather famous 2014 XGBoost presentation by Chen, p.33 refers to γ as the ” complexity cost by introducing additional leaf “. Now, a typical situation where we would tune gamma is when we use shallow trees as we try to combat over-fitting.
Why does gamma cause shallower trees in XGBoost cross?
Gamma causes shallower trees (or, at least, trees with fewer leaves), by restricting when splits will be made. I think the tutorial isn’t entirely clear/accurate.
What’s the difference between XGBoost and max depth?
XGBoost on the other hand make splits upto the max_depth specified and then start pruning the tree backwards and remove splits beyond which there is no positive gain. Another advantage is that sometimes a split of negative loss say -2 may be followed by a split of positive loss +10.