Abstract: A training method and system of a neural network model including a three-level model, and a prediction method and system are provided. The training method includes: acquiring a training data record; generating features of a training sample based on attribute information of the training data record, and using a label of the training data record as a label of the training sample; training the neural network model using a set of the training samples, learning an interaction representation between corresponding input items respectively by a plurality of intermediate models comprised in a second-level model of the neural network model, learning a prediction result at least based on the interaction representations output by the second-level model by a third-level model of the neural network model, and adjusting the neural network model at least based on a difference between the prediction result and the label.

Abstract: Provided are a simulation system and a simulation method, an epidemic deduction simulation system and an epidemic deduction simulation method.

Abstract: A method and a system for implementing a machine learning modeling process are provided. The method includes: obtaining configuration information set by a user for at least part of the machine learning modeling process, the configuration information representing an execution strategy of the at least part of the machine learning modeling process, and the configuration information including a first part whose execution strategy has been determined and a second part whose execution strategy is to be determined; determining the execution strategy of the second part by using automated machine learning; and executing the at least part of the machine learning modeling process based on the execution strategy of the first part and the determined execution strategy of the second part, to obtain a result corresponding to the at least part of the machine learning modeling process.

Abstract: Provided are a feature processing method and feature processing system for machine learning. The feature processing method includes: (A) acquiring a data record, wherein the data record comprises at least one piece of attribute information; (B) for each of the continuous features generated based on at least a some of the attribute information in the at least one piece of attribute information, executing a basic binning operation and at least one additional operation to generate a basic binning feature and at least one additional feature corresponding to each of the continuous features; and (C) generating a machine learning sample at least comprising the generated basic binning feature and at least one additional feature.

Abstract: The disclosure provides a training method and system of a neural network model including a three-level model, and a prediction method and system. The training method comprises: acquiring a training data record; generating features of a training sample based on attribute information of the training data record, and using a label of the training data record as a label of the training sample; training the neural network model using a set of the training samples, learning an interaction representation between corresponding input items respectively by a plurality of intermediate models comprised in a second-level model of the neural network model, learning a prediction result at least based on the interaction representations output by the second-level model by a third-level model of the neural network model, and adjusting the neural network model at least based on a difference between the prediction result and the label.

Abstract: Provided are a feature processing method and feature processing system for machine learning. The feature processing method includes: (A) acquiring a data record, wherein the data record comprises at least one piece of attribute information; (B) for each of the continuous features generated based on at least a some of the attribute information in the at least one piece of attribute information, executing a basic binning operation and at least one additional operation to generate a basic binning feature and at least one additional feature corresponding to each of the continuous features; and (C) generating a machine learning sample at least comprising the generated basic binning feature and at least one additional feature.