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Table 1 Statistical measures of the performance of the 10 machine learning algorithms and the median-based machine learning consensus predictor

From: Can human experts predict solubility better than computers?

  RMSE R2 ρ NC AAE
MLP 0.985 0.706 0.837 19 0.728
RF 1.165 0.583 0.736 20 0.802
Bagging 1.165 0.583 0.726 20 0.803
KNN 1.204 0.540 0.704 15 0.917
ExtraTrees 1.227 0.542 0.728 18 0.837
AdaBoost 1.235 0.545 0.708 19 0.851
PLS 1.265 0.507 0.670 15 0.980
SVM 1.280 0.520 0.694 16 0.925
SGD 1.429 0.577 0.752 11 1.185
Decision tree 1.813 0.260 0.530 17 1.198
ML median 1.140 0.601 0.762 18 0.778
  1. We assessed each machine learning method in terms of the root mean squared error (RMSE), coefficient of determination—which is the square of the Pearson correlation coefficient (R2), Spearman rank correlation coefficient (ρ), number of correct predictions within a margin of one log S unit (NC), and average absolute error (AAE)