List of postprocessor functions
When predicting with tree ensemble models, we sum the margin scores from individual trees and apply a postprocessor function to transform the sum into a final prediction. This function is also known as the link function.
Currently, Treelite supports the following postprocessor functions.
Element-wise postprocessor functions
identity
: The identity function. Do not apply any transformation to the margin score vector.signed_square
: Apply the functionf(x) = sign(x) * (x**2)
element-wise to the margin score vector.hinge
: Apply the functionf(x) = (1 if x > 0 else 0)
element-wise to the margin score vector.sigmoid
: Apply the sigmoid functionf(x) = 1/(1+exp(-sigmoid_alpha * x))
element-wise to the margin score vector, to transform margin scores into probability scores in the range[0, 1]
. Thesigmoid_alpha
parameter can be configured by the user.exponential
: Apply the exponential function (exp
) element-wise to the margin score vector.exponential_standard_ratio
: Apply the functionf(x) = exp2(-x / ratio_c)
element-wise to the margin score vector. Theratio_c
parameter can be configured by the user.logarithm_one_plus_exp
: Apply the functionf(x) = log(1 + exp(x))
element-wise to the margin score vector.
Row-wise postprocessor functions
identity_multiclass
: The identity function. Do not apply any transformation to the margin score vector.softmax
: Use the softmax functionf(x) = exp(x) / sum(exp(x))
to the margin score vector, to transform the margin scores into probability scores in the range[0, 1]
. Adding up the transformed scores for all classes will yield 1.multiclass_ova
: Apply the sigmoid functionf(x) = 1/(1+exp(-sigmoid_alpha * x))
element-wise to the margin scores. Thesigmoid_alpha
parameter can be configured by the user.