Abstract: A machine learning application method, a device, an electronic apparatus, and a storage medium, used to directly link service scenarios, aggregate data related to the service scenarios, accordingly explore modeling schemes, and ensure that data used in offline modeling scheme exploration and data used in an online model prediction service have the same source, thereby realizing consistency of source of offline and online data. Directly deploying an offline model to an online environment results in data inconsistency between online feature computation and offline feature computation, which in turn causes poor prediction performance; therefore, only a modeling scheme is deployed online, and the offline model is not deployed. After a modeling scheme is deployed online, sample data having a feature and feedback can be obtained by receiving a prediction request, thereby enabling model self-learning by means of the sample data.

Abstract: A visual interpretation method and a device for a logistic regression model, relating to the computer technology field. The method includes: receiving an interpretation request for a logistic regression model (S11); obtaining, according to the interpretation request, model parameters of the logistic regression model, the model parameters comprising each feature in the logistic regression model and a weight value of each feature (S12); aggregating each feature in the obtained model parameters according to a feature name (S13); obtaining feature statistics for each feature name to obtain feature statistics information for each feature name, wherein the feature statistics information indicates distribution information of weight values of each feature under the same feature name and/or dimension information of each feature under the same feature name (S14); and displaying the feature name and the corresponding feature statistics information using a graphical interface (S15).