Sequential Feedback Ensemble Model for Multilabel Classification

A sequential feedback prediction system predicts a risk of an event associated with a set of potential outcomes. The system receives training data including data records that have information associated with risk factors. Each data record may be labeled with outcomes that have inherent sequential dependencies. The system trains sub-models, each sub-model predicting risks for a respective outcome using a set of risk factors. For each sub-model, the system may determine a subset of sub-models whose results are used as input for the sub-model. The system determines an order to run the set of sub-models such that prediction results for preceding sub-models may be used as inputs for subsequent sub-models. The system may determine a number of rounds to run the ordered sequence of sub-models until the performance of the ensemble model meets a predetermined threshold. The system may generate a risk score for each of the set of outcomes.