CPC G06N 20/20 (2019.01) [G06F 18/213 (2023.01); G06F 18/2451 (2023.01)] | 17 Claims |
1. A computer implemented method of generating a gradient boosting decision tree for obtaining predictions, the method comprising:
combining values of low population feature values into a virtual bin;
fanning out the virtual bin into feature values having a low population;
finding split points by including the low population feature values into the split points and sorting variable values of a feature by their gradient during training of the gradient boosting decision tree;
performing a linear search to find a subset of variables with maximum split gain; and
modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search.
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