Disclosed herein are systems and methods for coding decision trees, such as for execution on a decision tree scorer system. A computing system determines, for a particular feature of a plurality of features included in one or more decision trees, a list of unique threshold values associated with the particular feature in the one or more decision trees. The computing system determines a plurality of threshold index values for the list of unique threshold values, and represents the one or more decision trees such that decision nodes of the one or more decision trees associated with the particular feature include ones of the threshold index values.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: determining for a particular feature of a plurality of features included in one or more decision trees, a list of unique threshold values associated with the particular feature in the one or more decision trees; determining a plurality of threshold index values for the list of unique threshold values; and representing the one or more decision trees such that decision nodes of the one or more decision trees associated with the particular feature include ones of the threshold index values. 2. The method of claim 1, further comprising, for a particular feature value of a set of feature values, the particular feature value associated with the particular feature: identifying a smallest one of the list of unique threshold values that is greater than or equal to the feature value; and coding the set of feature values to produce a coded set of feature values such that the particular feature value is represented in the coded set of feature values by a corresponding index feature value that is equal to a particular one of the sorted threshold index values that corresponds to the smallest one of the sorted list of unique threshold values. 3. The method of claim 2, further comprising transmitting the coded set of feature values to an on-chip decision tree processor. 4. The method of claim 2, further comprising scoring the one or more decision trees based at least in part on comparing ones of the sorted threshold index values to the corresponding index feature value. 5. The method of claim 1, further comprising scoring the one or more decision trees based at least in part on comparing ones of the sorted threshold index values to corresponding index feature values. 6. The method of claim 1, wherein the sorted list of unique threshold values are floating point values, and the plurality of sorted threshold index values are fixed point values. 7. The method of claim 1, further comprising determining a number of bits to represent the threshold index values based at least in part on a number of values in the sorted list of unique threshold values associated with the particular feature in the one or more decision trees. 8. The method of claim 7, further comprising: determining that the number of values in the sorted list of unique threshold values exceeds a predetermined number; and selecting, based on the number of values in the sorted list of unique threshold values exceeding the predetermined number, a plurality of n-bit words to represent the sorted threshold index values. 9. A computing system comprising: one or more processors; memory; one or more program modules stored on the memory and executable by the one or more processors to: determine for ones of a plurality of features included in decision nodes of one or more decision trees, lists of unique threshold values associated with the ones of the plurality of features; index the lists of unique threshold values to produce sets of threshold index values corresponding to the ones of the plurality of features in the decision nodes of the one or more decision trees; and code the one or more decision trees to produce one or more coded decision trees such that the decision nodes of the coded decision trees include ones of the sets of threshold index values. 10. The computing system of claim 9, wherein the one or more program modules are further executable by the one or more processors to determine a set of corresponding feature index values for a set of feature values such that outcomes of comparisons of the threshold index values to corresponding feature index values are equivalent to outcomes of comparisons of corresponding threshold values to corresponding feature values. 11. The computing system of claim 10, wherein the one or more program modules are further executable by the one or more processors to determine a set of corresponding feature index values for a set of feature values by: determining, for a particular feature value associated with a particular feature, whether any of the threshold values associated with the particular feature within the one or more decision trees is greater than or equal to the particular feature value; upon a determination that there is at least one threshold value associated with the particular feature within the one or more decision trees that is greater than or equal to the particular feature value, selecting a feature index value for the particular feature value that is equal to a threshold index value corresponding to a smallest one of the threshold values that is greater than or equal to the particular feature value; and upon a determination that there is no threshold value associated with the particular feature within the one or more decision trees that is greater than or equal to the particular feature value, selecting a feature index value for the particular feature value that differs from the threshold index values associated with the particular feature. 12. The computing system of claim 10, wherein the one or more program modules are further executable by the one or more processors to cause the set of corresponding feature index values to be transmitted to a decision tree-walking processor for scoring of the one or more decision trees against the set of feature values. 13. The computing system of claim 10, further comprising one or more coded decision tree-walking processors configured to score the one or more decision trees based on comparisons of ones of the threshold index values to ones of the corresponding feature index values for the set of feature values. 14. The computing system of claim 9, wherein the sorted list of unique threshold values are floating point values, and the plurality of sorted threshold index values are fixed point values. 15. The computing system of claim 9, wherein the one or more program modules are further executable by the one or more processors to determine a number of bits to represent ones of the sets of sorted threshold index values based at least in part on a number of values in the ones of the sets of sorted threshold index values. 16. One or more computer-readable storage media including a plurality of programming instructions that are executable by one or more processors of a computing system to: receive a plurality of decision trees, ones of the decision trees including decision nodes, ones of the decision nodes including threshold values and feature identifiers; identify sets of the threshold values, wherein the sets of the threshold values include threshold values that are present in decision nodes that correspond to the same feature identifiers; determine, for individual ones of the sets of threshold values, sorted lists of unique threshold values; establish, for individual ones of the sorted lists of unique threshold values, lists of threshold index values, wherein ones of the threshold index values correspond to ones of the unique threshold values within the lists of unique threshold values; and code the decision nodes of the plurality of decision trees such that their threshold values are represented by corresponding threshold index values of the lists of threshold index values. 17. The one or more computer-readable storage media of claim 16, wherein the plurality of programming instructions is further executable by the one or more processors to cause the computing system to: receive a set of feature values to be scored by the plurality of decision trees, individual feature values of the set of feature values corresponding to different ones of the feature identifiers; determine feature index values for the individual feature values such that: upon a determination that there is at least one threshold value in an associated list of unique threshold values that is either greater than or equal to a particular individual feature value, a particular feature index value for the particular individual feature value is equal to a particular threshold index value that is associated with a smallest one of the associated list of unique threshold values that is greater than or equal to the particular individual feature value; and upon a determination that none of the threshold values in the associated list of unique threshold values are either greater than or equal to the particular individual feature value, the particular feature index value for the particular individual feature being different from a largest one of the threshold index values corresponding to the associated list of unique threshold values; and code the set of feature values to produce a coded set of feature values such that the individual feature values are represented in the coded set of feature values by their corresponding index feature values. 18. The one or more computer-readable storage media of claim 16, wherein the plurality of programming instructions is further executable to determine numbers of bits to represent ones of the lists of threshold index values based at least in part on numbers of values in each of the lists of unique threshold values. 19. The one or more computer-readable storage media of claim 18, wherein the plurality of programming instructions is further executable to: determine that a particular number of values in a particular one of the lists of unique threshold values exceeds a predetermined number; and select, based on the particular number of values in the particular one of the list of unique threshold values exceeding the predetermined number, a plurality of n-bit words to represent the particular one of the list of threshold index values. 20. The one or more computer-readable storage media of claim 16, wherein the sorted lists of unique threshold values are floating point values, and the lists of threshold index values are fixed point values.