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Table 1 Candidate models obtained for log Pliver using the dataset reported by [11]

From: Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods

Model Predictive accuracy Subset cardinality #Frequent descriptors #Descriptors shared with other model
M1 (ALOGP, Mor29u, AMW, Se, Pol) R2 = 0.81
MAE = 0.15
RMSE = 0.20
5 2 2
M2 (ALOGP, SP15, RDF015v, RDF020e, H6v) R2 = 0.76
MAE = 0.17
RMSE = 0.23
5 1 1
M3 (ALOGP, Mor29u, X4Av, ESpm15,Mor31e, Ui) R2 = 0.79
MAE = 0.16
RMSE = 0.21
6 3 3
M4 (ALOGP, Mor29u, X4Av, DP06, QZZv, Mor02v, F01[C–C]) R2 = 0.79
MAE = 0.16
RMSE = 0.21
7 3 3
  1. The second column shows the predictive accuracy of the “best” model after applying 4-fold cross validation on three different methods (linear regression, decision trees, and neural networks). In this case, the best predictive accuracy for the four models was obtained by using a decision tree (M5P). The parameter setup and predictive accuracy for all methods are available in the Additional file 1: Table S1.