Hi,
1. In many(some ?) cases, modeling performance of JMP NN is better than that of others(i.e. python).
Yesterday, I tested and compared the NN results of JMP and python with their data (a few other data sets) in a semiconductor-related company. Based on R-square, python's results were 70%-80%, but JMP's was 95%-99%.
Why is JMP's analysis better ? In what cases would this result ?
2. Related question. Often, validation performance is better than training performance. In what cases would this result ?
When there is no clearly important factor ? When the number of data is not large ? When there are many same values in data ? When the number of nodes is large ?
I am wondering how to interpret this. Please help me. Thanks.
