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 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: A method for performing a machine learning process implementation is provided. The method comprises includes the following. Data is obtained. A labelling result of the data is obtained. A model framework matching a requirement of a user and/or a model matching a predicted target of the user is selected. Model training is performed using the data and the labelling result of the data based on the selected model framework and/or the selected model. The model framework is used to perform the model training on the basis of a machine learning algorithm.

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).

Abstract: Provided are method, apparatus and system for performing machine learning by using data to be exchanged. The apparatus includes: at least one computing device and at least one storage device storing instructions.

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: A user unsubscription prediction method and apparatus includes obtaining service consumption feature data, position activity feature data, and social network feature data of a user within a first preset time period, where the position activity feature data refers to data related to communication between the user and each base station within the first preset time period, and the social network feature data refers to data related to communication between the user and another user in a social network within the first preset time period, and inputting the obtained service consumption feature data, position activity feature data, and social network feature data to a pretrained classifier for calculation and outputting a calculation result, where the calculation result is a user unsubscription prediction result.

Abstract: A method and an apparatus for establishing and using a user recommendation model in a social network. The method includes obtaining training data from the social network, performing heterogeneous data transfer learning on the training data to learn a semanteme of the training data, establishing an association between a user and the image data by using the text data as a medium, establishing a semantic association relationship between the image data and the user according to the semanteme of the training data and the association between the user and the image data, and establishing a user recommendation model according to the semantic association relationship, where the user recommendation model includes the semantic association relationship between the image data and the user.

Abstract: Embodiments of the present invention provide a touch-control method, which is used for distinguishing a normal operation from a misoperation. The method provided by the embodiments of the present invention includes: receiving a touch instruction of a user; determining a user touch area corresponding to the touch instruction; determining whether a user observation area has an overlapping portion with the user touch area; and if the user observation area has an overlapping portion with the user touch area, responding to the touch instruction of the user. In the touch method provided by the embodiments of the present invention, a normal operation can be distinguished from a misoperation. The embodiments of the present invention further provide a related touch-control apparatus and terminal device.

Abstract: A method for determining a target user, includes: for any target service, acquiring historical data of multiple behavior objects that belong to a same service type as the target service; establishing a correspondence between user identifiers of different users and behavior object identifiers of different behavior objects of the same type; based on multiple established correspondences, constructing a data model that includes a user identifier and a behavior object identifier; using a value update rule to obtain, by means of calculation, a value of a probability that a user corresponding to each user identifier becomes a target user of the target service; and further using the value of the probability to select a target user of the target service, which can not only determine a target user group in a relatively open manner, but also effectively improve accuracy of and efficiency in determining a target user.

Abstract: Embodiments of the present invention provide a touch-control method, which is used for distinguishing a normal operation from a misoperation. The method provided by the embodiments of the present invention includes: receiving a touch instruction of a user; determining a user touch area corresponding to the touch instruction; determining whether a user observation area has an overlapping portion with the user touch area; and if the user observation area has an overlapping portion with the user touch area, responding to the touch instruction of the user. In the touch method provided by the embodiments of the present invention, a normal operation can be distinguished from a misoperation. The embodiments of the present invention further provide a related touch-control apparatus and terminal device.

Abstract: Embodiments of the present invention provide an optimization operation method and apparatus for a terminal interface. The method includes: acquiring hand operation information of a user by using an sensing apparatus on the terminal; determining, according to the hand operation information, a hand-holding manner in operating the terminal by the user and a hand parameter; acquiring an interface parameter on a current operation interface of the touchscreen; determining an operation blind area on the operation interface according to the hand-holding manner, the hand parameter, and the interface parameter; and performing optimization processing on an element in the operation blind area, so that the user can operate the element in the operation blind area in the hand-holding manner.