# Solved – XGBoost tree “Value” output:

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 `0` leaf `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 `value` = `log-odds`.

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