variable_importance: Calculate a simple measure of 'importance' for each feature.
In drf: Distributional Random Forests

Description Usage Arguments Value Examples

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

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

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

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

## Not run: 
# Train a quantile forest.
n <- 50
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)

## End(Not run)