PARALLELIZED BLOCK COORDINATE DESCENT FOR MACHINE LEARNED MODELS

Iterations of a machine learned model training process are performed until a convergence occurs. A fixed effects machine learned model is trained using a first machine learning algorithm. Residuals ofthe training of the fixed effects machine learned model are determined by comparing results of the trained fixed effects machine learned model to a first set of target results. A first random effectsmachine learned model is trained using a second machine learning algorithm and the residuals of the training of the fixed effects machine learned model. Residuals of the training of the first randomeffect machine learned model are determined by comparing results of the trained first random effects machine learned model to a second set of target result, in each subsequent iteration the training of the fixed effects machine learned model uses residuals of the training of a last machine learned model trained in a previous iteration. 执行机器学习模型训练过程的迭代直到发生收敛为止。使用第一机器学习算法来训练固定效应机器学习模型。通过将所训练的固定效应机器学习模型的结果与第一组目标结果进行比较来确定固定效应机器