The methods include, for instance: building model connections between models in a knowledgebase, which stores case data as model networks. An exploration probability stored in the knowledgebase indicates a likelihood of new connections based on a case data input to be employed for an analytical model of the case data input, which includes numerous stages and multiple model choices in each stage. Based on the new connections and model networks of the knowledgebase, paths are created and performance of respective paths/connections are evaluated. A predefined number of top performing paths are selected and the models and attributes that do not appear in the top performing paths are eliminated from the knowledgebase. Probabilities of model networks and a future case data input are updated accordingly and a best-fit model for the case data input is presented to a user.