Patent ID: 11893499
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 22:
23. A computer implemented method for developing and training models for analyzing data, the method comprising:
constructing a model of sequential layers, each layer including a single random forest, by:
receiving, by one or more computer processors, a training data set;
receiving, by the one or more computer processors, a determined number of trees per forest and a class vector specification;
growing, by the one or more computer processors, the number of determined trees for a first forest using the training data set;
determining, by the one or more computer processors, first Out-of-bag (OOB) predictions for an instance of the data set for the first forest;
appending the first OOB predictions as a probability vector to the training data set as new features represented in columns for the instance of the data set by the one or more computer processors;
determining an OOB accuracy for the first forest by the one or more computer processors;
growing the number of determined trees for an additional forest using the training data set with the appended first OOB predictions by the one or more computer processors;
determining an additional OOB prediction for the additional forest by the one or more computer processors;
appending additional OOB predictions to the training data set by the one or more computer processors;
determining an additional OOB accuracy for the additional forest by the one or more computer processors;
adding forests, by the one or more computer processors, until the additional OOB accuracy does not improve; and
combining an output of the additional forest by the one or more computer processors.