US 11,699,106 B2
Categorical feature enhancement mechanism for gradient boosting decision tree
Mohammad Zeeshan Siddiqui, Bellevue, WA (US); Thomas Finley, Bellevue, WA (US); and Sarthak Shah, Redmond, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Mar. 15, 2019, as Appl. No. 16/355,348.
Prior Publication US 2020/0293952 A1, Sep. 17, 2020
Int. Cl. G06N 20/20 (2019.01); G06F 18/213 (2023.01); G06F 18/2451 (2023.01)
CPC G06N 20/20 (2019.01) [G06F 18/213 (2023.01); G06F 18/2451 (2023.01)] 17 Claims
OG exemplary drawing
 
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.