24. The method of claim 17 wherein for both explicit and implicit conditions applying to a particular conditional variant of a model, learning conditional variants further comprises: forming a mini-model for a target attribute by statistically merging attribute data computed for a corresponding driving session; identifying pairs of like mini-models using a similarity metric; forming an implicit conditional variant model based on attribute data from pairs of like mini-models; and partitioning attribute data within each implicit conditional variant model into appropriate data buckets by explicit conditions.
17. The method of claim 16 wherein learning conditional variants comprises determining if at least one of an explicit and implicit condition applies to a particular conditional variant of a model.