Abstract: A data feature determining method includes: obtaining a to-be-processed data set; setting an initial selected feature set and an initial excluded feature set, and determining a candidate feature set; setting a maximum quantity of input model variables, a VIF threshold, and a minimum increment threshold of an AUC indicator of a model; traversing the candidate feature set to obtain a current-round traversal result; determining a maximum AUC value in the current-round traversal result, and determining whether a difference between the maximum AUC value in the current-round traversal result and a maximum AUC value in a previous-round traversal result is greater than the minimum increment threshold; if yes, removing a target feature based on the maximum quantity of the input model variables, and using the features in the selected feature set as the final data features; and if no, using the features in the selected feature set as the final data features.

Abstract: A data processing method includes: obtaining first sample data, and determining a target model and a feature set corresponding to the target model; obtaining second sample data, and dividing the second sample data into a development data set and a validation data set based on a predetermined proportion or a predetermined chronological order; respectively determining final sample data of the first sample data and retained sample data of the development data set based on the target model, the feature set corresponding to the target model, the first sample data, the development data set and the validation data set; and merging the final sample data and the retained sample data to obtain a modeling data set corresponding to a first business project. A data processing apparatus, and a computer device for implementing the data processing method are further provided.

Abstract: A data processing method includes: obtaining first sample data, and determining a target model and a feature set corresponding to the target model; obtaining second sample data, and dividing the second sample data into a development data set and a validation data set based on a predetermined proportion or a predetermined chronological order; respectively determining final sample data of the first sample data and retained sample data of the development data set based on the target model, the feature set corresponding to the target model, the first sample data, the development data set and the validation data set; and merging the final sample data and the retained sample data to obtain a modeling data set corresponding to a first business project. A data processing apparatus, and a computer device for implementing the data processing method are further provided.

Abstract: A business data processing method includes: step S1: extracting domestic business sample data; step S2: selecting a sample data set S by training a model; step S3: extracting overseas business sample data; step S4: merging the data set S and a data set T to obtain a data set A; step S5: setting an initial sample weight; step S6: setting a variable for a quantity of iterations; step S7: training the model based on a current weight to obtain a model At; step S8: calculating a loss et of the model At in the data set T; step S9: caching the model At and the loss et; step S10: updating a sample weight; step S11: updating the quantity of iterations t=t+1; step S12: determining whether a termination condition is met; and step S13: when the termination condition is met, determining a final model.

Abstract: A data feature processing method and a data feature processing apparatus are provided, which perform the following operations: sorting a plurality of groups of business data to obtain a business data sorting sequence, and determining a cross-time validation set and modeling sample data to establish a recognition model by using a preset classifier; calculating feature importance values of data features in the business data based on the recognition model and a gain indicator of the recognition model, and calculating a correlation matrix by taking the modeling sample data as a benchmark; determining to-be-selected model features based on the correlation matrix; and importing the to-be-selected model features into the preset classifier in batches to determine model benchmark performance data. In this way, a highly-correlated feature can be deleted based on an order of the feature importance values. This can reduce operation time and memory demands in a model establishment process.

Abstract: A business prediction method includes: obtaining a first business sample set and a second business sample set; performing training based on the first business sample set and the second business sample set to obtain a business prediction model, and predicting received to-be-predicted business information based on the business prediction model to obtain a business prediction result corresponding to the received to-be-predicted business information. A business prediction apparatus is further provided. The business prediction method and the business prediction apparatus take into account data features of some business samples of being rejected in a business validation, while considering business samples of passing the business validation. This restores a business scenario, reduces the waste of costs of the rejected samples, and balances demands for a modeling sample and a rejected sample reasonably when there are insufficient samples of passing the business validation.