Closed damar-wicaksono closed 7 years ago
The data frame aggregates the results from all experimental configuration in the challenge problem to one observation for a given number of training samples, regression formula, design experiment, covariance and transformation.
err_sandia <- data.frame(pc1_q2 = c(), pc1_rmse = c(),
pc2_q2 = c(), pc2_rmse = c(),
pc3_q2 = c(), pc3_rmse = c(),
rec_err = c(),
cov_type = c(),
doe = c(),
reg = c(),
n = c(),
transf_name = c())
A possible complication might arise from the fact that there are two layers of output in a Principal component model.
The first is the principal component score metamodeling error using the usual metric, Q2 and RMSE. The second is the reconstruction error from using a truncated principal component coupled with the score predicted by the metamodel. The error here will be more intuitive.