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 function f(x) = sign(x) * (x**2) element-wise to the margin score vector.
hinge: Apply the function f(x) = (1 if x > 0 else 0) element-wise to the margin score vector.
sigmoid: Apply the sigmoid function f(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]. The sigmoid_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 function f(x) = exp2(-x / ratio_c) element-wise to the margin score vector. The ratio_c parameter can be configured by the user.
logarithm_one_plus_exp: Apply the function f(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 function f(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 function f(x) = 1/(1+exp(-sigmoid_alpha * x)) element-wise to the margin scores. The sigmoid_alpha parameter can be configured by the user.