I don't believe there is any ordering implied by the columns _N1, _N2,..  They are just showing the top K nearest neighbors, not necessarily ordered by the nearest to farthest since they all have equal weight when scoring. ... View more

You could try doing variable selection with the HP Variable Selection node (on the HPDM tab).  With unsupervised selection (an option for the Target Model property), it analyzes variance and reduces dimensionality by forward selection of the variables that contribute the most to the overall data variance.  Or you can do sequential selection which first performs unsupervised selection, then does supervised selection where the target is taken into account.   ... View more

You're absolutely right - it is likely due to collinearity among your inputs.  Are you using a model selection method in the Scorecard node?  That might help eliminate the problem.   ... View more

This paper has some code that might be helpful: http://www2.sas.com/proceedings/forum2007/132-2007.pdf   ... View more

Yes, when you create a data source with the Data Source Wizard, you should see and be able to select views as well as data sets when you browse to one of your SAS libraries. ... View more

This is from the SAS Enterprise Miner Reference Help, hope this answers your question:   Lift is traditionally not calculated for Sequence Analysis. In an Association analysis, the lift is the proportion of time that the right-hand side (RHS) occurs, given that the left-hand-side (LHS) has occurred. This computation is straightforward when there is only one LHS and one RHS, which is the case in Association. The computation becomes much more complex when you have a sequence that has more than one step. In the sequence, A -> B -> C -> D , are you looking at the subset of events where A -> B -> C occurred and then D occurred? Or, are you interested in looking at A -> B and then later C -> D ? There is not a unique way to create a lift. Therefore, lift is not calculated. ... View more

Here was a response from SAS employee John Stultz:   In case you have not seen this, here is an old but good paper by SAS’ Terry Woodfield (Predictive Modeling in the Insurance Industry Using SAS Software-- http://www2.sas.com/proceedings/sugi26/p013-26.pdf ) that might help give you some ideas on how to create derived fields and use them as model inputs within an Enterprise Miner process flow.    You might also find more current information/examples in Global Forum papers by searching the Online Proceedings: http://supportprod.unx.sas.com/events/sasglobalforum/previous/online.html   And as always, you can usually find a bunch of stuff by searching the internet.  For example, here is a list of some derived/binary variables that might be relevant: (https://www.researchgate.net/publication/227540405_Detection_of_Automobile_Insurance_Fraud_With_Discrete_Choice_Models_and_Misclassified_Claims)       Characteristics of the Insured/Claimant/Policy: AGE: Age of insured driver when the accident occurred LICENSE: Number of years since the insured obtained first driver’s license RECORDS: Number of previous claims of the insured COVERAGE: Third-party liability equals 1; extended coverage equals 0 DEDUCTIBLE: Existence of a deductible equals 1; otherwise equals 0 ACCESSORI: Coverage for accessories equals 1; otherwise equals 0   Characteristics of the Vehicle: VEHUSE: Vehicle for private use equals 1; other uses equal 0 VEHAGE: Age of the vehicle   Characteristics of the Accident: FAULT: Insured accepts the blame for the accident equals 1; other-wise equals 0 NONURBAN: Accident occurred in a nonurban area equals 1; otherwise equals 0 NIGHT: Accident occurred at night equals 1; otherwise equals 0 WEEKEND: Accident occurred during a weekend equals 1; otherwise equals 0 WITNESS: Existence of witnesses equals 1; otherwise equals 0 POLICE: Existence of police report equals 1; otherwise equals 0 ZONE1: Zone with high level of accidents equals 1; otherwise equals 0 ZONE3: Zone with low level of accidents equals 1; otherwise equals 0 REPORT: Existence of a suspicious textual report equals 1; otherwise equals 0. This variable indicates that the claimant reported unusual circumstances for the accident. NAMES: Same family name for insured and the other vehicle driver equals 1; otherwise equals 0. PROXIM: Accident occurred between the policy issue date and the policy effective starting date equals 1; otherwise equals 0. DELAY: Claim not reported to the company within the established period equals 1; otherwise equals 0   ... View more