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Table 1 Summary of the results (10-fold cross validation) obtained for all the models

From: Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons

Feature selection Feature selection technique Number of variables/PC Machine learning model RMSE¤ q2cv§
No   1485 RF 50.28 0.9303
   1485 SVM 44.47 0.9520
Yes PCA* 28 PC# SVM 34.87 0.9671
  GA° 58 SVM 47.1 0.9391
  RF - VIǂ 89 SVM 34.1 0.9686
  1. ¤Root Mean Square Error; §cross validated squared correlation coefficient; Random Forests; Support Vector Machines; *Principal Components Analysis; # Principal Components; ° Genetic Algorithms; ǂVariable Importance using Random Forests.