Hi! I think what you're looking for might be the "dtreeExportModel" action in the decisionTree action set. This can take models created using the outmodel option and convert them to the type that you'd see from the savestate statement.   I'm not 100% sure of your setup, but try something like this: proc cas; action decisionTree.dtreeExportModel / table = {name="RF_Model_V1"}, casout = {name="name_of_model_astore", replace=1}, vote="PROB"; run; quit;   I put the "replace=1" there so that it will overwrite previous tables if they have the same name (use carefully). I also put vote="PROB", which is the default voting method for PROC FOREST. You will want to specify to ensure that the scoring method matches what you expect - probability voting amongst the trees in the forest as opposed to majority voting amongst the trees in the forest.   I hope this helps, and feel free to reach out if you have any additional questions! ... View more

EduxEdux,   The linearExplainer action, which supports the SHAP Kernel method for estimating Shapley values does require a "predicted target." However, in your case, you have multiple "predicted targets" as the paper about Shapley values on autotunecoders suggests. While the autoencoder is trying to predict what each input variable is, the linearExplainer action wants to know the name of the output variable.   The only complication I can see is if the annTrain autoencode has the same output variable names as input variable names. As long as they are different names, derived or otherwise, then you should be able to use the linearExplainer action.   For the "predictedTarget" parameter, instead use the output variable name corresponding to the input variable that you wish to examine. As the paper says, you first need to consider the top variables which are different between input and output (and thus you'd need different names in SAS anyway, otherwise you'd overwrite). When you go to use the linearExplainer action, use that output variable name as the "predictedTarget".   Hopefully this is clear, let me know if I can help further!   -Ralph ... View more

It seems that you actually have two questions here: 1) How do I compare two clustering results to determine which is optimal 2) How do I determine the number of clusters is optimal.   While 1) can be related to 2), if you want to compare a clustering result with 3 clusters vs a result with 20 clusters, I will mostly address these separately. I will have some specific details in my answer, but also more general points. I hope both help!     1) "How do I compare two clustering results to determine which is optimal"   As mentioned, by Ksharp, the Analysis of Variance is a useful metric to use when considering clustering metrics. You can use PROC GLM in an Enterprise Miner code node to do this.     Ultimately though, as clustering is an unsupervised task (ie there is no target variable used), I find that the meaning of "optimal" in the case of clustering can be problem dependent (even if the data is the same).   The way I like to approach the question is by first asking "what is the goal of clustering" for the context of the problem you're working on (what do you want the clusters to help you do?). For example, if it's a predictive modeling problem in which you want to develop models on each cluster separately, then the overall accuracy of your models across all the data will let you know how good the clustering is.     2) "How do I determine the number of clusters when using clustering"   One way, if you have SAS Enterprise Miner 13.1 or later, is the HP Cluster node under the HPDM tab. This node has a metric called the "Aligned Box Criterion" which automatically seeks to find the number of clusters for you.   Another method is called spectral clustering, which is looks at the eigenvalues of a similarity matrix to try to determine the number of clusters. While this is not implemented in Enterprise Miner, SAS does have the procedures so that you could implement it yourself using a SAS Code node, with data step and a procedure to get the principal components, followed by kmeans.   ----   Finally, an idea to address both questions that is much more involved, is consensus clustering (which can be used with the two previous ideas for determining the number of clusters). The goal behind consensus clustering is to ensemble multiple clustering results into one (including results with different numbers of clusters). The reasoning for why you would want to ensemble is that if multiple clustering results overlap, then you feel confident that the areas of overlap are "correct" / "optimal." Again, this is not implemented in Enterprise Miner, and is quite involved. That being said, it is possible to do using SAS data step code and the procedures / nodes in Enterprise Miner.   Hopefully some of this helps, either immediately, or by giving you things to think about.   ... View more

Yingjian's post has some very good points. I wanted to add a bit more, and also some about clustering in Enterprise Miner.   In Enterprise Miner there is the "Cluster" node that is under the Explore tab. This node uses PROC CLUSTER to compute the clustering. In this node there is a Cubic Clustering Criterion (CCC) that attempts to determine the number of clusters while performing the analysis. In general, there aren't many ways to accurately gain a good view of how many clusters there should be a priori, unless information is known about the data before hand.   The best results (average, centroid, ward) requires your definition of best. In centroid based methods, many people will try to define best by looking at the total sum of distances from points to their respective centroid, but in non-centroid based methods this is no longer a useful measure. Ultimately I think the results of your later analysis may be how you want to determine which of the clustering results was "best."   Also available in Enterprise Miner 12.3 and on, there is the "HP Cluster" node that is under the HPDM tab. This node uses PROC HPCLUS to run kmeans clustering. PROC HPCLUS does have the Aligned Box Criterion (ABC) that Yingjian mentioned to determine the number of clusters. If you have the chance to try the HP Cluster node, you may find that it has some capabilities that you would find useful.   ---   Also, to address the "best" number of clusters question, Yingjian is correct in that it is very difficult to say what number is best. Even defining what best means in the clustering context can be difficult.   If you are unsatisfied with the results of a single clustering, there is an approach that you can try called consensus clustering. This approach is to cluster the data multiple times and attempt to ensemble the results of all the clustering runs into one final clustering. Enterprise Miner has no node that will do this for you automatically, but you can do this using multiple PROC CLUSTER calls and some other data step code. This would require a SAS code node, but is an interesting approach if you're looking to do something more (it might require a bit of research to get started though). ... View more