Abstract: Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.

Abstract: A system and method are described herein for training and evaluating machine learning models trained using large complex datasets in an efficient manner. The systems enable methods for training a first model and generating predictions using the first trained model. The predictions and a portion of the data may be used to train a second model and generate predictions as an output of the second model. The first model may be used on a third dataset to generate predictions, which are appended to the third dataset. The appended third dataset is then input into the second model for generation of additional outputs that are compared against the third dataset for accuracy.