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Figure 2 | BMC Bioinformatics

Figure 2

From: Using epigenomics data to predict gene expression in lung cancer

Figure 2

Performance comparison of models with various feature selection and classification methods. The Areas Under the Curve (AUC) of ROC are used as the metric to compare the performance of models with different combinations of feature selection (CFS, Gain Ratios and ReliefF) and classification (Gaussian SVM, Linear SVM, Logistic regression, Naïve Bayes and Random Forest), on the training data with 10 fold cross-validation. The model with ReliefF based feature selection and Random Forest classification is selected as the best model.

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