LOGISTIC REGRESSION GRADIENT CALCULATION METHOD AND APPARATUS

The present disclosure provides logistic regression gradient calculation methods and apparatuses. One exemplary calculation method comprises: acquiring training data, the training data including X-row user data and Y-row click-through data corresponding to the X-row user data; converting the X-row user data into X-column data; segmenting the X-column data and a weight vector to form N X-column data segmentation blocks and N weight vector segmentation blocks; starting N threads respectively to generate N sub-logistic regression gradients according to the N X-column data segmentation blocks, the N weight vector segmentation blocks, and the corresponding Y-row click-through data; and splicing the N sub-logistic regression gradients to form a full logistic regression gradient. With embodiments of the present disclosure, a computing machine can support training of a super-large-scale logistic regression model, which increases the calculation speed, shortens the training time, and greatly reduces the memory usage of the computing machine.