|
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 −∞
