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This is optional. BPMN diagram generated Diagram. Using Camunda Modeler, open Converted. This is expected as there is nothing in the exported BPMN to indicate any coordinates.
That will be addressed in the next section. And here is the process in Camunda Modeler after the missing diagram has been generated and pool has been adjusted accordingly:. If diagram fidelity is desired and you can export your processes in a.
You can even use the workspace you created earlier in the tutorial. Stephanie Wilkerson. Arch A. Mostafa Abuelkhair. Lakshya Agarwal. S Akshatha. Zak Al Hashash. Zakaria Al Hashash. Ziyad Alrumaih. Issam Alsawalmeh. Mohan Ananthanarayanan. Milen Angelov. Nour Assy. Andrea Avallone. Meredith Bailey. David Bailie. Add a Feature Selection Modeling node and run it.
Edit the resulting generated model. Note that a number of variables are predictive of the target. There is no substitute for lots of hard work during Data Understanding. Some of the patterns here could be capitalized upon, and others could indicate the need for data cleaning.
The Using the Feature Selection node creatively to remove or decapitate perfect predictors recipe in Chapter 2 , Data Preparation — Select , shows how circular logic can creep into our analysis.
Note the large number of data and amount-related variables in the Generated model. These variables indicate that the potential donor did not give in those time periods. Failing to give in one time period is predicted with failing to give in another; it makes sense.
Is this the best way to get at this? Perhaps a simple count would do the trick, or perhaps the number of recent donations versus total donations. It is the distance between the first and second donation. What would be a common reason that it would be NULL? Obviously the lack of a second donation could cause that problem. Perhaps analyzing new donors and established donors separately could be a good way of tackling this.
Note that neither imputing with the mean, nor filling with zero would be a good idea at all. We have no reason to think that one time and two time donors are similar. We also know for a fact that the time distance is never zero.
What might cause this variable to be missing, and for the missing status alone to be predictive? Perhaps we need a new donor to be on the mailing list for a substantial time before our list vendor can provide us that information. It is quite common that the data miner has to rely on others to either provide data or interpret data, or both.
Even when the data miner is working with data from their own organization there will be input variables that they don't have direct access to, or that are outside their day-to-day experience.
Are zero values normal? What about negative values? Null values? Are balance inquiries in a month even possible? The concept of outliers is something that all analysts are familiar with. Even novice users of Modeler could easily find a dozen ways of identifying some. This recipe is about identifying outliers systematically and quickly so that you can produce a report designed to inspire curiosity. There is no presumption that the data is in error, or that they should be removed.
It is simply an attempt to put the information in the hands of Subject Matter Experts, so quirky values can be discussed in the earliest phases of the projects. It is important to provide whichever primary keys are necessary for the SMEs to look up the records.
On one of the author's recent projects, the team started calling these reports quirk reports. We will start with the Outlier Report. Adjust the stream options to allow for 25 rows to be shown in a data preview.
We will be using the preview feature later in the recipe. Add a Statistics node. These three have either unusually high maximums or surprising negative values as shown in the Data Audit node.
Add a Sort node. It is important to work with your SME to know which variables put quirky values into context. Reverse the sort, now choosing descending order, and preview the Filter node. Consider the following screenshot for later use:. Preview the Filter node. Examine Outliers. There is no deep theoretical foundation to this recipe; it is as straightforward as it seems. It is simply a way of quickly getting information to an SME. They will not be frequent Modeler users.
Also summary statistics only give them a part of the story. Providing the min, max, mean and median alone will not allow an SME to give you the information that you need.
If there is a usual min such as a negative value, you need to know how many negatives there are, and need at least a handful of actual examples with IDs.
An SME might look up to values in their own resources and the net result could be the addition of more variables to the analysis. Alternatively, negative values might be turned into nulls or zeros. Negative values might be deemed out of scope and removed from the analysis. There is no way to know until you assess why they are negative. Sometimes values that are exactly zero are of interest. High values, NULL values, and rare categories are all of potential interest.
The most important thing is to be curious and pleasantly persistent and to inspire collaborators to be curious as well. The Removing redundant variables using correlation matrices recipe in Chapter 2 , Data Preparation — Select.
Model instability would typically be described as an issue most noticeably during the evaluation phase. Model instability usually manifests itself as a substantially stronger performance on the Train data set than on the Test data set. This bodes ill for the performance of the model on new data; in other words, it bodes ill for the practical application of the model to any business problem. Veteran data miners see this coming well before the evaluation phase, however, or at least they hope they do.
The trick is to spot one of the most common causes; model instability is much more likely to occur when the same inputs are competing for the same variance in the model. In other words, when the inputs are correlated with each other to a large degree, it can cause problems.
The data miner can also get themselves into hot water with their own behavior or imprudence. Overfitting , discussed in the Introduction of Chapter 7 , Modeling — Assessment, Evaluation, Deployment, and Monitoring , can also cause model instability.
The trick is to spot potential problems early. If the issue is in the set of inputs, this recipe can help to identify which inputs are at issue. The correlation matrix recipe and other data reduction recipes can assist in corrective action. This recipe also serves as a cautionary tale about giving the Feature Selection node a heavier burden than it is capable of carrying.
This node looks at the bivariate relationships of inputs with the target. Bivariate simply means two variables and it means that Feature Selection is blind to what might happen when lots of inputs attempt to collaborate together to predict the target.
Bivariate analyses are not without value, they are critical to the Data Understanding phase, but the goal of the data miner is to recruit a team of variables. The team's performance is based upon a number of factors, only one of which is the ability of each input to predict the target variable.
To detect potential model instability using the Partition and Feature Selection nodes, perform the following steps:. Edit the Partition node, click on the Generate seed button, and run it. Since you will not get the same seed as the figure shown, your results will differ.
This is not a concern. In fact, it helps illustrate the point behind the recipe. Run the Feature Selection Modeling node and then edit the resulting generated model.
Note the ranking of potential inputs may differ if the seed is different. Edit the Partition node, generate a new seed, and then run the Feature Selection again. Edit the Feature Selection generated model. For a third and final time, edit the Partition node, generate a new seed, and then run the Feature Selection.
Edit the generated model. At first glance, one might anticipate no major problems ahead. Clearly it provides some value, so what is the danger in proceeding to the next phase? The change in ranking from seed to seed is revealing something important about this set of variables. These variables are behaving like variables that are similar to each other.
They are all descriptions of past donation behavior at different times. The larger the number after the underscore, the further back in time they represent. Frankly, there is a good chance that it is the most predictive, but these variables are fighting over top status in the small decimal places of this analysis.
We can trust Feature Selection to alert us that they are potentially important, but it is dangerous to trust the ranking under these circumstances, and it certainly doesn't mean than if we were to restrict our inputs to the top ten that we would get a good model. The behavior revealed here is not a good indication of how these variables will behave in a model, a classification tree, or any other multiple input techniques. The variable used to form the second branch would likely not be the second variable on the list because the first and second variables are similar to each other.
Each situation is different, but perhaps the best option here is to identify what these related variables have in common and distill it into a smaller set of variables. To the extent that these variables have a unique contribution to make—perhaps in the magnitude of their distance in the past—that too could be brought into higher relief during data preparation.
Keith McCormick is an independent data miner, trainer, conference speaker, and author.
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