Combine 6 parameters and 64 bins

Today, I combined 6 parameters and 64 bins to improve the prediction accuracy, but it did not show improvement in accuracy of prediction.

Summary:

I compared the prediction of both 6 parameters and 64 bins.

The first figure is T2 distribution comparison of 64 bins.

The second figure is T2 distribution comparison of 6 parameters.

The third is 3 line comparison figure.

For 6 parameters line, the main problem is that the miu may be quite different from the actual one.
For 64 bins line, the main problem is that the peak may be quite different from the actual one.

After moving the bigger miu of 6 parameters to the more accurate one with 64 bins data, , it is a little better compared with original 6 parameters prediction, but not for original 64 bins prediction. The reason is that: although I adjust miu for 6 parameters, it will not change alpha and sigma, both of which will all impact the accuracy of T2 distribution.

In my opinion, I should divide my findings into two parts. Maybe later, I can divide it into two papers.
First, I choose logging data, set different kind of categories,predict these categories and fit T2 distribution with 6 parameters of normal distribution and change the ANN model to predict 6 parameters for predicting t2 distribution.
Second, I choose logging data, set different kind of categories,predict these categories and change the ANN model to predict t2 distribution directly.

Although median value of R2 of 6 parameters is a little smaller than that of 64 bins, it is more stable too. The median value of R2 of 6 parameters drops from 0.7553 (training) to 0.7220 (testing) by 0.0333 but the median value of R2 of 64 bins drops from 0.8445 (training) to 0.7662 (testing) by 0.0783.

Tomorrow, I want to discuss with you and start to write the paper.