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TRAINING OR USING SETS OF EXPLAINABLE MACHINE-LEARNING MODELING ALGORITHMS FOR PREDICTING TIMING OF EVENTS
TRAINING OR USING SETS OF EXPLAINABLE MACHINE-LEARNING MODELING ALGORITHMS FOR PREDICTING TIMING OF EVENTS
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机译:培训或使用一组可解释的机器学习建模算法,用于预测事件的时间
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摘要
Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.
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