Hello all, I have a score from a binary classification model (probability of some event of interest). This score was generated by an external provider, I don't have access to the underlying data or model logic, but I have seen the model metrics which indicate it is strong. I want to augment this prediction with additional data attributes (that was not available to the external provider). There are 3 ways of doing this: 1) Use the probability score as a direct input into a new model which also consumes the additional data (leads to instability) 2) Create a new model using the additional data and ensemble the probability score of this model with the external model (has the potential to hurt the overall prediction) 3) Use an incremental learning approach (similar to boosting), set the probability score of the external model as the baseline and then incrementally increase the fit using the additional data Incremental learning is possible using python and the xgboost package , keen to understand if its possible using either SAS EM or base code? Has anyone attempted anything like this? Many thanks, Shane
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