Understanding tree structure in R gbm package

I am having some difficulty understanding how the trees are structured in R's gbm gradient boosted machine package. Specifically, looking at the output of the pretty.gbm.tree Which features do the indices in SplitVar point to?

I trained a GBM on a dataset, here is the top ~quarter of one of my trees -- the result of a call to pretty.gbm.tree:

   SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight   Prediction
0         9  6.250000e+01        1         2          21      0.6634681   5981  0.005000061
1        -1  1.895699e-12       -1        -1          -1      0.0000000   3013  0.018956988
2        31  4.462500e+02        3         4          20      1.0083722   2968 -0.009168477
3        -1  1.388483e-22       -1        -1          -1      0.0000000   1430  0.013884830
4        38  5.500000e+00        5        18          19      1.5748155   1538 -0.030602956
5        24  7.530000e+03        6        13          17      2.8329899    361 -0.078738904
6        41  2.750000e+01        7        11          12      2.2499063    334 -0.064752766
7        28 -3.155000e+02        8         9          10      1.5516610     57 -0.243675567
8        -1 -3.379312e-11       -1        -1          -1      0.0000000     45 -0.337931219
9        -1  1.922333e-10       -1        -1          -1      0.0000000     12  0.109783128
```

It looks to me here that the indices are 0 based, from looking at how LeftNode, RightNode, and MissingNode point to different rows. When testing this out by using data samples and following it down the tree to their prediction, I get the correct answer when I consider SplitVar to be using 1 based indexing.

However, 1 of the many trees I build has a zero in the SplitVar column! Here is this tree:

SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight    Prediction
0         4  1.462500e+02        1         2          21      0.41887   5981  0.0021651262
1        -1  4.117688e-22       -1        -1          -1      0.00000    512  0.0411768781
2         4  1.472500e+02        3         4          20      1.05222   5469 -0.0014870985
3        -1 -2.062798e-11       -1        -1          -1      0.00000     23 -0.2062797579
4         0  4.750000e+00        5         6          19      0.65424   5446 -0.0006222011
5        -1  3.564879e-23       -1        -1          -1      0.00000   4897  0.0035648788
6        28 -3.195000e+02        7        11          18      1.39452    549 -0.0379703437

What is the correct way to view the indexing used by gbm's trees?

The first column that is printed when you use the pretty.gbm.tree is the row.names that is assigned in the script pretty.gbm.tree.R. In the script, the row.names is assigned as row.names(temp) <- 0:(nrow(temp)-1) where temp is the tree information stored in data.frame form. The right way to interpret the row.names is to read it as the node_id with the root node being assigned a 0 value.

In your example:

Id SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction 0 9 6.250000e+01 1 2 21 0.6634681 5981 0.005000061

means that the root node (indicated by the row number 0) is split by the 9-th split variable (the numbering of the split variable here starts from 0, so the split variable is the 10th column in the training set x). SplitCodePred of 6.25 denotes that all points less than 6.25 went to the LeftNode 1 and all points greater than 6.25 went to RightNode 2. All points that had a missing value in this column were assigned to the MissingNode 21. The ErrorReduction was 0.6634 due to this split and there were 5981 (Weight) in the root node. Prediction of 0.005 denotes the value assigned to all values at this node before the point was split. In the case of terminal nodes (or leaves) denoted by -1 in SplitVar, LeftNode, RightNode, and MissingNode, the Prediction denotes the value predicted for all the points belonging to this leaf node adjusted (times) times the shrinkage.

To understand the tree structure, its important to note that the splitting of the tree happens in a depth first fashion. So when the root node (with node id 0) is split into its left node and right node, the left side is processed until no further splits are possible before returning and labeling the right node. In both the trees in your example, the RightNode gets a value of 2. This is because in both cases, the LeftNode turns out to be a leaf node.