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)
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.
# 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)
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