cal_imp_func: Variable importance scores for the robust boosting algorithm...

In RRBoost: A Robust Boosting Algorithm

Description

This function calculates variable importance scores for a previously computed RRBoost fit.

Usage

cal_imp_func(model, x_val, y_val, trace = FALSE)

Arguments

model	an object returned by Boost
x_val	predictor matrix for validation data (matrix/dataframe)
y_val	response vector for validation data (vector/dataframe)
trace	logical indicating whether to print the variable under calculation for monitoring progress (defaults to FALSE)

Details

This function computes permutation variable importance scores given an object returned by Boost and a validation data set.

Value

a vector of permutation variable importance scores (one per explanatory variable)

Author(s)

Xiaomeng Ju, [e-mail]

Examples

data(airfoil)
n <- nrow(airfoil)
n0 <- floor( 0.2 * n )
set.seed(123)
idx_test <- sample(n, n0)
idx_train <- sample((1:n)[-idx_test], floor( 0.6 * n ) )
idx_val <- (1:n)[ -c(idx_test, idx_train) ]
xx <- airfoil[, -6]
yy <- airfoil$y
xtrain <- xx[ idx_train, ]
ytrain <- yy[ idx_train ]
xval <- xx[ idx_val, ]
yval <- yy[ idx_val ]
xtest <- xx[ idx_test, ]
ytest <- yy[ idx_test ]
model = Boost(x_train = xtrain, y_train = ytrain,
     x_val = xval, y_val = yval,
     type = "RRBoost", error = "rmse",
     y_init = "LADTree", max_depth = 1, niter = 1000,
     control = Boost.control(max_depth_init = 2,
           min_leaf_size_init = 20, save_tree = TRUE,
           make_prediction =  FALSE, cal_imp = FALSE))
var_importance <-  cal_imp_func(model, x_val = xval, y_val= yval)

RRBoost documentation built on Oct. 23, 2020, 7:11 p.m.