Abstract: The present disclosure describes techniques for determining performance of a classifier. A first machine learning model and a second machine learning model may be trained by aggregating updates to the first machine learning model and the second machine learning model received from a plurality of client computing devices. A cumulative distribution function (CDF) associated with a distribution of the positive samples in the user data may be estimated using the trained first machine learning model. A probability density function (PDF) associated with a distribution of the negative samples in the user data may be estimated using the trained second machine learning model. An integration-based computation of an area under the receiver operating characteristic curve (AUC) of the classifier may be performed using the PDF and the CDF.

Abstract: The present disclosure provides a method and apparatus for transmitting data of a self-driving vehicle, a device and a storage medium, wherein the method comprises: distinguishing data generated during travel of the self-driving vehicle into real-time data and non-real-time data; transmitting the real-time data into a server through a wireless network in real time; storing the non-real-time data into a vehicle-mounted storage device so that after the self-driving vehicle ends travel, the data stored in the vehicle-mounted storage device are transmitted into the server in a predetermined manner. The solution of the present application may be applied to ensure data integrity.