`predictorImportance`

computes estimates of predictor
importance for `tree`

by summing changes in the
mean squared error (MSE) due to splits on every predictor and dividing
the sum by the number of branch nodes. If the tree is grown without
surrogate splits, this sum is taken over best splits found at each
branch node. If the tree is grown with surrogate splits, this sum
is taken over all splits at each branch node including surrogate splits. `imp`

has
one element for each input predictor in the data used to train this
tree. At each node, MSE is estimated as node error weighted by the
node probability. Variable importance associated with this split is
computed as the difference between MSE for the parent node and the
total MSE for the two children.

Estimates of predictor importance do not depend on the order
of predictors if you use surrogate splits, but do depend on the order
if you do not use surrogate splits.

If you use surrogate splits, `predictorImportance`

computes
estimates before the tree is reduced by pruning or merging leaves.
If you do not use surrogate splits, `predictorImportance`

computes
estimates after the tree is reduced by pruning or merging leaves.
Therefore, reducing the tree by pruning affects the predictor importance
for a tree grown without surrogate splits, and does not affect the
predictor importance for a tree grown with surrogate splits.