Abstract: The present disclosure discloses a system and method for batch and real-time feature computation, and the system includes: a management server, one or more batch feature computing units, one or more distributed computing units, one or more real-time feature computing units, an offline database and a real-time database. The management server receives a script containing feature computing logics, starts batch feature computation in a batch feature computing scenario, and deploys the script in a real-time feature computing scenario to generate a real-time computing plan, wherein the script containing the feature computing logics has unity for batch feature computing logics and real-time feature computing logics, and the real-time database obtains and stores data for the real-time feature computation from the offline database. According to the present disclosure, the batch feature computing logics and the real-time feature computing logics may be unified by the script.

Abstract: Provided are a programmable device for processing a data set, and a method for processing a data set. The programmable device includes a plurality of accumulation circuits, wherein each of the accumulation circuits includes a pipeline adder and a cache unit for storing a computation result of the pipeline adder; and a multiplexer for receiving in sequence data in a data set, dynamically determining a correlation between a plurality of features included in the data and the plurality of accumulation circuits, and respectively sending, according to the correlation, feature values of the plurality of features in the received data to corresponding accumulation circuits.

Abstract: A data exchange method, a data exchange device, and a computing device for data exchange between a provider and a recipient for machine learning, the method including: (a) receiving a machine learning model from the provider (S1100); (b) respectively transforming output data samples into corresponding output eigenvectors by utilizing the machine learning model from the provider (S1200); (c) after transformation, combining the output eigenvectors with corresponding identifiers to form exchange samples (S1300). According to the data exchange method, original data is transformed into vector information which cannot be restored but can be applied to machine learning, for use in exchange, so as to, on one hand, enable efficient use of data for machine learning and, on the other hand, prevent unauthorized use, sale or disclosure of the original data.

Abstract: A method and device for presenting a prediction model, and a method and device for adjusting a prediction model. The method for presenting a prediction model includes: obtaining at least one prediction result of a prediction model for at least one prediction sample; obtaining at least one decision-making tree training sample for training a decision-making tree model according to the at least one prediction sample and the at least one prediction result, the decision-making tree model being used for fitting the prediction model; training the decision-making tree model by using at least one decision-making tree training sample; and visually presenting the trained decision-making tree model. By means of the method, a prediction model hard to understand can be approximated to a decision-making tree model, and the approximated decision-making tree model is presented, so that a user better understands the prediction model according to the presented decision-making tree model.

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: A method and device for presenting a prediction model, and a method and device for adjusting a prediction model. The method for presenting a prediction model includes: obtaining at least one prediction result of a prediction model for at least one prediction sample; obtaining at least one decision-making tree training sample for training a decision-making tree model according to the at least one prediction sample and the at least one prediction result, the decision-making tree model being used for fitting the prediction model; training the decision-making tree model by using at least one decision-making tree training sample; and visually presenting the trained decision-making tree model. By means of the method, a prediction model hard to understand can be approximated to a decision-making tree model, and the approximated decision-making tree model is presented, so that a user better understands the prediction model according to the presented decision-making tree model.