method | R2 | RMSE | MAE | MSE | SMAPE |
|---|
DL | 0.73±0.05 | 1.85±0.26 | 1.31±0.12 | 3.33±0.99 | 0.34±0.04 |
SVM (linear) | 0.78±0.06 | 1.81±0.18 | 1.34±0.13 | 3.27±0.60 | 0.34±0.05 |
MLP | 0.76±0.06 | 1.82±0.13 | 1.36±0.11 | 3.29±0.71 | 0.34±0.04 |
SVM (kernel) | 0.74±0.06 | 1.82±0.25 | 1.35±0.13 | 3.37±1.06 | 0.33±0.03 |
RF | 0.72±0.04 | 1.83±0.26 | 1.32±0.15 | 3.41±1.01 | 0.35±0.03 |
DT | 0.48±0.11 | 2.46±0.28 | 1.80±0.18 | 6.11±1.38 | 0.49±0.07 |
k-NN | 0.41±0.10 | 2.65±0.35 | 1.95±0.22 | 7.12±1.85 | 0.53±0.06 |
- Performance of the learned models with the different methods evaluated with the different metrics, expressed in the format “average value ± standard deviation”, obtained on 100 executions. DT: decision tree. k-NN: k-nearest neighbors. DL: deep neural network with 3 hidden layers and weight decay. MLP: multi-layer perceptron neural network. RF: random forest. SVM (kernel): support vector machine with kernel. SVM (linear): linear support vector machine. RMSE: root mean square error. MAE: mean absolute error. MSE: mean square error. SMAPE: symmetric mean absolute percentage error. R2: coefficient of determination. RMSE, MAE, MSE, SMAPE: best value 0.00 and worst value +∞. R2: best value 1.00 and worst value −∞
