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How does an XGBoost model work?
XGBoost is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm, which attempts to accurately predict a target variable by combining the estimates of a set of simpler, weaker models.
Which of the following approaches is used to determine where a split occurs in a decision tree?
Reduction in Variance is a method for splitting the node used when the target variable is continuous, i.e., regression problems. It is so-called because it uses variance as a measure for deciding the feature on which node is split into child nodes.
How does the splitting algorithm work in XGBoost?
It’s also worth noting that random forest doesn’t simply select features at random — for each split, random forest selects a subset of features, and then chooses the best split from among the random subset. The splitting algorithm for XgBoost is pre-sorted and histogram (which is what Joel described in his answer).
How is feature selection handled in XGBoost?
A remark on Sandeep’s answer: Assuming 2 of your features are highly colinear (say equal 99% of time) Indeed only 1 feature is selected at each split, but for the next split, the xgb can select the other feature. Therefore, the xgb feature ranking will probably rank the 2 colinear features equally.
Is there such a thing as equal ranking for XGBoost?
See here for explainations. Remark on PSAfrance’s answer, there is no such thing as equal ranking for 2 collinear features for xgb as tested by @dalloliogm. In fact, the equal ranking might be a case for random forests as the informational value of two correlated features is split due to random bagging.
How is the gain metric calculated in XGBoost?
The second column is the Gain metric which implies the relative contribution of the corresponding feature to the model calculated by taking each feature’s contribution for each tree in the model. A higher value of this metric when compared to another feature implies it is more important for generating a prediction.