Table 1.
The performance of age predicting models trained on expression profiles on the test set.
| Model | Best parameters | r [f; m] | R2 | MAE (years) | ε-accuracy |
|---|---|---|---|---|---|
| k-nearest neighbors | Auto algorithm; N of neighbors of 5; distance as weights | 0.78 [0.79; 0.76] | 0.64 [0.67; 0.62] | 9.73 [9.5; 9.8] | 0.58 [0.60; 0.56] |
| Random forest | N trees of 700 with max depth of 50 | 0.84 [0.88; 0.82] | 0.69 [0.71; 0.66] | 9.54 [9.2; 9.7] | 0.66 [0.67; 0.63] |
| ElasticNet | Alpha of 0.001 and L1 ratio of 0.2 | 0.88 [0.92; 0.87] | 0.78 [0.84; 0.76] | 7.37 [7.0; 7.66] | 0.83 [0.84; 0.79] |
| Support vector machines | Linear kernel with cost of 0.01 | 0.91 [0.95; 0.80] | 0.83 [0.89; 0.80] | 7.20 [6.1; 6.5] | 0.87 [0.89; 0.85] |
| Deep feature selection model | Adam optimizer with lr of 10−5; 3 hidden layers (512, 256, 128 units); l1, l2 and frobenius norm regularizers; ELU activation function; Dropout of 0.5 | 0.91 [0.96; 0.89] | 0.83 [0.92; 0.78] | 6.24 [5.6; 8.1] | 0.80 [0.83, 0. 78] |
r for Pearson correlation coefficient; R2 for coefficient of determination; MAE for mean absolute error, that shows the average disagreement between actual chronological and predicted ages; ε-accuracy the accuracy of prediction within a period, which was calculated for ε of 10 years; f for metrics calculated only for female samples and m for male.