4 results about "Class variable" patented technology

A computer hardware system includes a slice generator and a policy generator and performs the following. The slice generator slices a first dataset including true values and predicted values of a class variable into a plurality of slices each defining a plurality of observations within the first dataset. A first one and another one of the plurality of slices are selected, and a union of observations is generated by adding observations within the selected another one to observations within the selected first one of the plurality of slices. The selecting another one of the plurality of slices and the generating the union is repeated until a number of observations within the union reaches a predetermined value. Using the policy generator and after the number of observations within the union reaches the predetermined value, an error policy is generated. The predicted values were generated by a machine learning engine.

The invention relates to the technical field of machine learning, in particular to a multi-hospital joint causal feature selection method and system under privacy protection. According to the method, each hospital client firstly generates randomized feature representation based on local original data and uploads the randomized feature representation to a server to participate in a condition independence test; secondly, the server constructs a candidate feature set according to the correlation between the features and the class variables, the features having a causal relationship with the class variables are screened through an iterative conditional independence test, and a conditional set is dynamically adjusted in the test process to eliminate redundant features; and finally, outputting a global causal feature subset which can solve the problem of data heterogeneity in a federated environment and enhance the prediction performance of the model, so that each client can be used for federated model training. According to the method, causal features can be efficiently and safely identified in a federated learning environment, the generalization ability and interpretability of the model are improved, and meanwhile data privacy is protected.

Methods and apparatuses for evaluating probabilistic fairness of machine learning (ML) classification models include a server that generates a first input data set, including assigning a class membership label to each of a plurality of participants based upon a probability of class membership derived from a surrogate class variable. The server generates a second input data set, including assigning a class membership label to each of the plurality of participants based upon ground truth class values. The server executes a binary classification model on the first input data set to generate inferred fairness metrics for the binary classification model. The server executes the binary classification model on the second input data set to generate actual fairness metrics for the binary classification model. The server determines a disparity in one or more fairness metrics for the binary classification model based upon a comparison of the inferred fairness metrics to the actual fairness metrics.

The present invention relates to the technical field of data dynamic processing, and discloses a data dynamic processing method, device, storage medium and apparatus. When receiving data to be processed, the present invention determines the class variable information and business logic information of a preset processing function; assigns values to the class variable information in the preset processing function according to the data to be processed through a preset rule to obtain the assigned class variable information; and performs data processing on the assigned class variable information according to the business logic information, so as to assign values to the data to be processed through the preset processing function, implement the business logic processing of the data to be processed, and achieve the purpose of improving the flexibility of data dynamic processing.