Calculate a simple measure of 'importance' for each feature.

A simple weighted sum of how many times feature i was split on at each depth in the forest.

variable_importance(forest, decay.exponent = 2, max.depth = 4)

Arguments

forest	The trained forest.
decay.exponent	A tuning parameter that controls the importance of split depth.
max.depth	Maximum depth of splits to consider.

Value

A list specifying an 'importance value' for each feature.

Examples

# \donttest{
# Train a quantile forest.
n <- 250
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
q.forest <- quantile_forest(X, Y, quantiles = c(0.1, 0.5, 0.9))

# Calculate the 'importance' of each feature.
variable_importance(q.forest)
#>             [,1]
#>  [1,] 0.25744941
#>  [2,] 0.05249690
#>  [3,] 0.06866620
#>  [4,] 0.05559858
#>  [5,] 0.11015747
#>  [6,] 0.07746014
#>  [7,] 0.06883904
#>  [8,] 0.08823708
#>  [9,] 0.12806624
#> [10,] 0.09302893
# }