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Using the following R code I obtain a decision tree using the agaricus dataset:
data(agaricus.train, package='xgboost') bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3, eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic") # plot all the trees xgb.plot.tree(model = bst) # plot only the first tree and display the node ID: xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
I want to understand more clearly the "value" output of the tree (the 3rd line in the oval shaped object). Here we can see that tree
7 gives a
value 1.90174532. (That is the first terminal node in the image). I want to know if this
value is the same as the
log-odds score. So, all observations which follow the upper path of the decision tree will obtain a log-odds score of
1.90174532. Then in a new decision tree the observations will fall into a different split depending on each observations characteristics and will obtain a "new"
value Then we sum up all these
values across all trees to obtain a final
log-odds score which can then be converted to a predicted probability using the logistic function.
Is my intuition correct? Does