Hi All,
I have searched in the JMP community about SHAP Values and I am wondering if someone can direct me to some resources in how to evaluate SHAP values using JMP Pro 17. I understand how to populate the SHAP values (from the prediction profiler), but creating the graphs are a bit confusing at the point. I used the graph builder to build the graph, but I am wondering if there are videos or guides on the types of analyses that I can do with SHAP values and if there's an automatic way to create graphs from the SHAP platform.
Thank you for taking the time to get back to me.
Sincerely,
Nisha
When you add shapley values to your data table you will also get a table script which creates a graph for you
You can change the type if wish to (stacked bar chart (default), stacked line chart, stacked area chart all work on some level).
You could also stack the data and visualize that. If your data doesn't have unique identifier for each row, you might want to create one and then stack the shap columns and keep that unique column. After you have stacked your data, you can create a bit different looking plots, for example
Link Mark provided has some information about the shapley values in JMP and some sources to check out.
There is also Shapley Values: Explaining Individual Predicted Probabilities and
I'm not sure which outputs you mean for graphs. Second plot you provided looks like it could be from shap python package Welcome to the SHAP documentation — SHAP latest documentation and not from JMP (you can get quite similar in JMP but it does require some work). SHAP documentation does also have additional examples you can use ideas how you could plot your data.
After you have your shapley values in your graph, you can start using them in any way you find useful in JMP. Usually I think stacking them is best option, you can find stack platfrom from Tables menu Stack Columns in Data Tables (jmp.com). Stack the shapley value columns (+ feature value and row identifier if you want to) and then start plotting. I would still keep the other format as both of these have their own pros and cons.
Shapley values are available from the Profiler.
Thank you @Mark_Bailey ! I did take a look at the guide before, but it didn't provide any detail on the process needed to create different types of graphs and how to evaluate/interpret those graphs, which is what I am looking for. Thank you for sharing the link.
When you add shapley values to your data table you will also get a table script which creates a graph for you
You can change the type if wish to (stacked bar chart (default), stacked line chart, stacked area chart all work on some level).
You could also stack the data and visualize that. If your data doesn't have unique identifier for each row, you might want to create one and then stack the shap columns and keep that unique column. After you have stacked your data, you can create a bit different looking plots, for example
Link Mark provided has some information about the shapley values in JMP and some sources to check out.
There is also Shapley Values: Explaining Individual Predicted Probabilities and
TO
I'm not sure which outputs you mean for graphs. Second plot you provided looks like it could be from shap python package Welcome to the SHAP documentation — SHAP latest documentation and not from JMP (you can get quite similar in JMP but it does require some work). SHAP documentation does also have additional examples you can use ideas how you could plot your data.
After you have your shapley values in your graph, you can start using them in any way you find useful in JMP. Usually I think stacking them is best option, you can find stack platfrom from Tables menu Stack Columns in Data Tables (jmp.com). Stack the shapley value columns (+ feature value and row identifier if you want to) and then start plotting. I would still keep the other format as both of these have their own pros and cons.
Hi Jarmo @jthi ,
You are right about the second plot, it is from shap python package, but I checked out the SHAP documentation that you recommended for additional examples and found it to be helpful in visualizing the data. Thank you for providing the direction in creating the graphs using the stack columns (this is new to me and I explored more to learn about it).
Thank you,
Nisha
I found the following website to provide examples of interpreting different types of SHAP plots (though not all inclusive) and may be of help to others:
https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/