Deep forest model development and training
AssigneeInternational Business Machines Corporation
InventorsJing Xu, Rui Wang, Xiao Ming Ma, Ji Hui Yang, Xue Ying Zhang, Jing James Xu, Si Er Han
Life of this patent
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Abstract
Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.
Claims25
A computer implemented method for developing and training models for analyzing data, the method comprising:
- constructing a model by:
- growing, by one or more computer processors, a random forest of decision trees from a data set;
- determining, by the one or more computer processors, Out-of-bag (OOB) predictions for an instance of the data set for the random forest;
- appending the Out-of-bag predictions as a probability vector to the data set as new features represented in columns for the instance of the data set by the one or more computer processors;
- growing, by the one or more computer processors, an additional random forest using the data set with the appended OOB predictions;
- and
- combining an output of the additional random forest, by the one or more computer processors, with a combiner.
A computer program product for developing and training models for analyzing data, the computer program product comprising one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
- programmed instructions for constructing a model by:
- providing a data set;
- growing a random forest of decision trees from the data set;
- determining Out-of-bag (OOB) predictions for an instance of the data set for the random forest;
- appending the Out-of-bag predictions as a probability vector to the data set as new features represented in columns for the instance of the data set;
- growing an additional random forest using the data set with the appended OOB predictions;
- and
- combining an output of the additional random forest with a combiner.
A computer system for developing and training models for analyzing data, the computer system comprising:
- one or more computer processors;
- one or more computer readable storage devices;
- program instructions stored on the one or more computer readable storage devices for execution by the one or more computer processors, the program instructions comprising:
- programmed instructions for constructing a model by:
- providing a data set;
- growing a random forest of decision trees from the data set;
- determining an Out-of-bag (OOB) predictions for an instance of the data set for the random forest;
- appending the Out-of-bag predictions as a probability vector to the data set as new features represented in columns for the instance of the data set;
- growing an additional random forest using the data set with the appended OOB predictions;
- and
- combining an output of the additional random forest with a combiner.
A computer implemented method for developing and training models for analyzing data, the method comprising:
- constructing a model by:
- receiving, by one or more computer processors, a training data set;
- growing, by the one or more computer processors, a random forest of decision trees from the training data set;
- determining, by the one or more computer processors, Out-of-bag (OOB) predictions for an instance of the data set for the random forest;
- appending the Out-of-bag predictions as a probability vector to the training data set as new features represented in columns for the instance of the training data set by the one or more computer processors;
- determining, by the one or more computer processors, a first OOB accuracy for the random forest;
- growing, by the one or more computer processors, an additional random forest using the training data set with the appended OOB predictions;
- determining, by the one or more computer processors, a second OOB accuracy for the additional random forest;
- comparing, by the one or more computer processors, the first OOB accuracy of the random forest and the second OOB accuracy of the additional random forest;
- and
- combining an output of the additional random forest by the one or more computer processors with a combiner.
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.
Dependencies are inferred from the claim text.