Service model training and online update methods and apparatuses

A computing device, comprising a memory and a processor, wherein the memory stores executable code, and when executing the executable code, the processor is caused to implement a service model training method, wherein the service model is a weighted form of several base learners, and the method comprises:

• multiple training periods added with a base learner, and in a single training period:
• obtaining each training sample, wherein a single training sample has a corresponding service feature and service target value, and a predicted value obtained by processing the service feature by using a current service model;
• determining a first gradient vector of the current service model on each training sample, wherein a single element of the first gradient vector corresponds to a gradient determined based on a single training sample;
• separately performing orthogonal decomposition of the first gradient vector for each base learner in the current service model, so as to update the first gradient vector by using a gradient component perpendicular to each base vector, wherein a single base vector is formed by a predicted value of a corresponding single base learner for each training sample;
• and
• obtaining a new base learner and its weight with an objective of minimizing a residual determined according to an updated first gradient vector, so as to update the current service model.