In cutting our individuals data in more than one group , we can obtain more than one model. In using the SVM classification to automatically classify new data we obtain this result.
The result is better on all result. The best fit is for two or three classes. After this threshold the model become more unstable.
Portioning of ours classes
| Classe 1 | Classe 2 | Classe 3 | Classe 4 | |
| 4 classes | 110 | 441 | 351 | 192 |
| 3 classes | 381 | 596 | 117 | |
| 2 classes | 626 | 468 |
Nb classes
|
RMSE
|
MAE
|
MSE
|
ARV
|
|
Linear regression
|
2
|
0.13763
|
0.099092
|
0.018942
|
0.51857
|
3
|
0.13501
|
0.096144
|
0.018227
|
0.49899
|
|
4
|
0.21412
|
0.13089
|
0.045847
|
1.2551
|
|
PLS regression
|
2
|
0.13245
|
0.094019
|
0.017542
|
0.48025
|
3
|
0.13047
|
0.091678
|
0.017021
|
0.466
|
|
4
|
0.14783
|
0.10862
|
0.021853
|
0.59828
|
|
SVM Polynomial
|
2
|
0.12929
|
0.092427
|
0.016715
|
0.4576
|
3
|
0.12763
|
0.09117
|
0.016288
|
0.44593
|
|
4
|
0.14822
|
0.1032
|
0.021969
|
0.60146
|
|
Neural network
|
2
|
0.13692
|
0.10066
|
0.018747
|
0.51323
|
3
|
0.17428
|
0.13354
|
0.030374
|
0.83156
|
|
4
|
0.18433
|
0.13045
|
0.033976
|
0.93016
|
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