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A project is considered a collection of builds, and each build is a collection of nodes. Nodes are averaged to obtain a final predicted value for the build. For each node in a build, multiple neural networks are constructed and the best performing neural network is used as that node’s predictor. Using multiple nodes allows a build to learn from multiple learning and validation sets, reducing the build’s error.

ECNet: Large scale machine learning projects for fuel property prediction