I'm using JMP pro to create models of some process data that include 11 factors and a response. What I would like to do is predict the value of the response with a confidence interval (or similar statistic) based on specific values of the factors. This is possible to do with the prediction profiler using linear models under the fit model platform.
The problem is that the neural network model seems to do much better than others with this data and when I fix the values of the factors, I get a predicted response value with no statistic on how accurate this prediction is (like the confidence intervals in the fit model platform). Is there a way to get more information on this prediction? Thanks.
Have you taken a look at the Save Bagged Predictions option within the Profiler hot spot? This will save a bagged mean prediction along with standard error out to the data table.
Thanks. That looks great. Is there a way to get these with the standard error or standard deviation into a profiler? Or is the only way to make future predictions of response is by entering factor values in a new row in the table and checking the result?
I'm asking because I'll have to share the results within the company and am trying to figure out the best way to do this. I know the profiler can be saved as html.
Although it's integrated with many platforms, (using 'Graph > Profiler') the Profiler also allows you to profile the formula columns that are saved from these platforms (or indeed, any other formula column). So you should be able to get what you need.
I should have added that 'share results . . .' can mean many things - If your use case requires making predictions on a more 'industrial scale' you should look at teh ability of the Formula Depot to generate score code.