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Table 2 Median prediction interval width at confidence levels from 10 to 99%

From: A confidence predictor for logD using conformal regression and a support-vector machine

Epsilon Nonconformity measure Confidence level
10% 20% 30% 40% 50% 60% 70% 80% 90% 95% 99%
\(10^{-3}\) Abs-diff 0.109 0.221 0.336 0.462 0.604 0.771 0.986 1.284 1.813 2.237 3.841
Normalized 0.122 0.243 0.362 0.478 0.595 0.718 0.854 1.027 1.319 1.649 2.892
Log-normalized, \(\beta =0\) 0.071 0.155 0.257 0.387 0.560 0.801 1.171 1.812 3.291 5.273 10.879
Log-normalized, \(\beta =1\) 0.074 0.159 0.260 0.384 0.545 0.763 1.080 1.599 2.689 4.031 7.676
\(10^{-4}\) Abs-diff 0.069 0.139 0.211 0.288 0.378 0.486 0.629 0.843 1.245 1.695 3.006
Normalized 0.079 0.157 0.233 0.311 0.395 0.491 0.610 0.789 1.200 1.918 7.194
Log-normalized, \(\beta =0\) 0.042 0.094 0.159 0.243 0.352 0.519 0.772 1.223 2.311 3.918 10.157
Log-normalized, \(\beta =1\) 0.044 0.097 0.163 0.245 0.356 0.509 0.741 1.137 2.030 3.233 7.204
\(10^{-5}\) Abs-diff 0.065 0.132 0.201 0.270 0.354 0.459 0.600 0.813 1.217 1.680 3.024
Normalized 0.075 0.148 0.220 0.293 0.376 0.474 0.605 0.824 1.445 2.664 12.199
Log-normalized, \(\beta =0\) 0.041 0.092 0.155 0.234 0.341 0.495 0.738 1.171 2.205 3.747 10.007
Log-normalized, \(\beta =1\) 0.042 0.095 0.158 0.235 0.339 0.486 0.710 1.095 1.963 3.156 7.247
  1. Shown are MPI at confidence levels (validity) from 10 to 99%. Note that a smaller median prediction interval indicates higher efficiency of a nonconformity measure. Shown are results for models with \({\textit{cost}}=1\) and epsilon values \(10^{-3}\), \(10^{-4}\) and \(10^{-5}\). Italicized are results for the best model at each epsilon value and confidence level. Marked by bolditalics are results for overall best models at each confidence level