Skip to main content
Fig. 4 | BMC Bioinformatics

Fig. 4

From: A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Fig. 4

Number of features selected for each model at initial feature-selection stage and elastic net stage. The left axis denotes the number of features (logarithm scaled) with the right axis showing the MAE for each model. The left y-axis refers to the blue and orange bars while the right y-axis corresponds to the red x-shaped markers. The model that utilised PCA in advance of elastic net (to the right of the vertical black line) is shown apart, as PCA is technically a feature transformation method and, as such, the feature count refers to the number of principal components (transformed features). The number of features shown for those models that utilise explicit feature rankings (mutual information, Pearson’s correlation, linear SVR and random forest) pertain, in each case, to the optimal model from all models tested with ranked feature sets in defined steps between 50 and 20,000 (as specified in “Feature-selection methods” Section)

Back to article page