Function to determine permutation importance, and optionally plot results. based on R2 value.
[pij, R2] = permutation_importance(x, y, W, repeats) [pij, R2] = permutation_importance(x, y, W, repeats, sf) [pij, R2] = permutation_importance(x, y, W, repeats, sf, af) [pij, R2] = permutation_importance(x, y, W, repeats, sf, af, fig) [pij, R2] = permutation_importance(x, y, W, repeats, sf, af, fig, labels)
n x 1 vector describing overall variable importance
m x repeats matrix of resulting R2 values
n x m matrix of input parameters, each column represents a different parameter
n x p matrix of target values, each column represents a different variable
hypermatrix of weights and biases resulting from trained neural network
integer value determining how many permutations to execute
string variable identifing simulation function, default 'ann_FFBP_run'
matrix of strings describing activation function for each layer incl output, default = ['ann_tansig_activ','ann_purelin_activ']
boolean variable indicating if plots should be made or suppressed, default %f
row vector of strings describing the input variables
Function to investigate permutation variable importance of trained neural network