In an approach to improving accuracy through weak model aggregation, one or more computer processors generating a plurality of hyperparameter sets, wherein each hyperparameter set in the plurality of hyperparameter sets contains one or more hyperparameters varied to increase over-training in one or more models, wherein over-training includes overfitting or underfitting. The one or more computer processors create a plurality of weak models utilizing a created bootstrap dataset in a plurality of created bootstrap datasets, a corresponding extracted explanatory variable set, and a corresponding hyperparameter set in the generated plurality of hyperparameter sets, wherein each weak model in a created plurality of weak models shares at least the created bootstrap dataset, the extracted explanatory variable set, the generated hyperparameter set, a machine learning technique, or a model architecture. The one or more computer processors predict a classification for an unknown datapoint by aggregating the created plurality of weak models.