Presentation Type

Event

Track

Methodology

Abstract

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as a poster at the SDSU Data Symposium.

Start Date

5-2-2019 3:30 PM

End Date

5-2-2019 4:30 PM

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Feb 5th, 3:30 PM Feb 5th, 4:30 PM

Neural Shrubs: Using Neural Networks to Improve Decision Trees

Pasque 255

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as a poster at the SDSU Data Symposium.