Predict by classes

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

Predict - Perceptron - Neural Network

The percepton or the neural network approach is really an interesting method. Based on some method that use learning the time to obtain a good model is more long than other linear or mathematical study.

For verify the model evolution we use a validation period.

This is the result obtained with the neural network solution.

RMSE=0.11787
The overlearning permit to detect som instability during the learning . The best result is obtained with 40 round.

Compare round and error in learning and validation. 

Compare three neurons size on learning errors.