1. A method of generating and using data models in a mobile device, comprising: receiving, in a processor of the mobile device from a server computing device, a full classifier model, the received full classifier model including a plurality of decision nodes, each decision node in the plurality of decision nodes including a test condition and a weight value; collecting mobile device-specific information in the mobile device; identifying combinations of features that require monitoring and analysis in the mobile device, and determining a relative importance of each of the identified feature combinations, based on the collected mobile device-specific information; culling, via the processor of the mobile device, the received full classifier model to generate a lean classifier model that includes a subset of the plurality of decision nodes included in the received full classifier model, the culling comprising: identifying decision nodes included in the received full classifier model that include test conditions relevant to evaluating the identified feature combinations; prioritizing the identified decision nodes based on determined relative importance of the identified feature combinations; and generating the lean classifier model to include only the identified decision nodes, ordered in accordance with their priority; generating a behavior vector that characterizes a behavior of the mobile device; and applying, by the processor of the mobile device, the generated behavior vector to the generated lean classifier model to classify the behavior of the mobile device.