We will start with the Outlier Report.str stream that uses the TELE_CHURN_preprep data set.

To create an Outlier report:

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.