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Table 1 Performance of classifiers and feature selection methods

From: Machine learning methods to predict child posttraumatic stress: a proof of concept study

Classifier   All features Feature selection with HITON-PC
SVM linear observed data 0.79 (0.02)** 0.68 (0.04)*
label shuffling 0.50 [0.32 0.71] 0.50 [0.36 0.67]
SVM poly observed data 0.78 (0.02)* 0.68 (0.04)
label shuffling 0.50 [0.31 0.71] 0.50 [0.34 0.71]
SVM RB observed data 0.76 (0.02)* 0.68 (0.04)
label shuffling 0.50 [0.36 0.70] 0.50 [0.35 0.69]
Random forest observed data 0.78 (0.01)** 0.74 (0.01)*
label shuffling 0.50 [0.33 0.67] 0.50 [0.33 0.73]
Lasso observed data 0.67 (0.01)** 0.74 (0.01)*
label shuffling 0.50 [0.44 0.57] 0.50 [0.35 0.68]
Logistic Regression (LR) observed data 0.47 (0.01) 0.72 (0.01)
label shuffling 0.50 [0.35 0.64] 0.51 [0.32 0.74]
Stepwise LR observed data 0.57 (0.02) 0.72 (0.02)*
label shuffling 0.51 [0.39 0.64] 0.49 [0.31 0.71]
  1. The performance (measured as Area Under the ROC Curve) of individual classifiers and feature selection methods in the observed data and under the null hypothesis of no signal in the data (estimated with label shuffling). For observed results the mean and (standard deviation) were presented. For the label shuffling, mean and [95% confidence interval] were presented. Predictive performance that are significant at p < 0.05 is labeled with *, predictive performance that are significant at p < 0.01 is labeled with **