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 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.