Hi, Subham.   Unfortunately, the Boosting node in EM is not designed to output information about the individual trees beyond the number of leaves.   The EM node invokes PROC TREEBOOST to build the boosted model.  An expert user might know how to run PROC TREEBOOST directly in an EM Code node.  In that case, one could include the RULES= dataset, STATSBYNODE= dataset, and the TOPOLOGY=dataset options in the SAVE statement.  Together they would describe all the splits and node statistics.  However, they are not documented or really supported by tech support for the boosting node.   In my opinion multicolinearity is not a problem.   The boosting algorithm does not invert a matrix, so there is no concern about matrix ranks.   Regarding the ratio variable, if there are no other missing values in the data, then simply set the -99 values to missing.  "Missing" is a special value that boosting will assign to the best branch independently of the other values.  The algorithm also considers splitting Missing vs Non-Missing.   If the ratio variable contains other missing values that you do not want to merge with the special -99 value, then one approach is to create a second variable with value 0 for observations with non-special ratio values and 1 for observations with the special value.  In the original ratio variable, replace the -99 value with the average of the legitimate values.  Setting it to the average value is an attempt to make those observations uninfluential in the split search.   If there are more special values than just -99, then create a separate value for each in the second variable, and declare that variable nominal instead of binary.   This approach is just an idea.  I cannot think of a better approach.   Good luck.   Padraic   ... View more

A weight is a positive number.   "Weighting observations" means a positive number is associated with each observation, and the algorithm utilizes that number somehow.  Intuitively, observations with larger weights influence the algorithm more than observations with smaller weights.  When  Adaboost has created 10 trees in its boosting model, it will assign small weights to observations it is predicting well, so that Adaboost will create the 11th tree focusing on observations it hitherto predicted poorly.   "Weighting trees" means the predictions from the trees are multiplied by a weight:  P(X) = W1 T1(X) + W2 T2(X), where Ti(X) is the prediction of tree i for inputs X, and Wi are the weights.  Sometimes the gradient boosting algorith is explained: first train the next tree (T2), and then find a single number (W2) that works best. ... View more

Hi. No, SAS EM does not think you only have 86.72 events.  The display is adjusting the counts to reflect the adjusted priors. There might be a display setting that turns the adjustment off (I don't know).  In any case, the computational code knows about all the observations.    The adjustments can change the computations in three places: 1. Depending on user-properties, the split search will act as if there are 86.72 events or as if there are 1301 events. 2. Depending on user-properties, the tree can be retrospectively pruned based on the adjusted numbers or the unadjusted numbers.  3. The posterior probabilities will be adjusted.  I am guessing that the default behaviour for 1 and 2 is to not incorporate the adjustments.  Why: because typically the adjustment makes the event look more rare, and rare events typically fool trees into being too small.   As you point out, the data step code coming out of logistic includes code at the end to adjust the posterior probabilities.   The decision tree code does not output corresponding code because it outputs posterior probabilities that are already adjusted:  decision tree computes the adjustments before outputing the data step code.   Let us know if you still have questions. -Padraic   ... View more