Evaluation index determination method and system
A technology for evaluating indicators and determining methods. It is applied in neural learning methods, neural architectures, and biological neural network models. It can solve the problems of complex evaluation model construction and poor reuse of new data.
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Embodiment 1
[0024] Embodiments of the present invention provide a method for determining an evaluation index, which is applicable to application scenarios where evaluation of target objects is required. like figure 1 As shown, the determination method of the evaluation index includes:
[0025] Step S1: Obtain the sample size of the object to be evaluated, the preset evaluation index data and the target evaluation variables.
[0026] In the embodiment of the present invention, the subjects to be evaluated can be students, teachers, schools, etc. In practical applications, the number of samples, preset evaluation index data and target evaluation variables can be reasonably selected according to the specific conditions of the evaluated objects. For example, when the objects to be evaluated are students, the optional sample size is the number of all students in a school or all students in an online learning course, and the default evaluation indicators are basic statistical indicators in the...
Embodiment 2
[0076] An embodiment of the present invention provides a system for determining evaluation indicators, such as Figure 4 As shown, the system includes:
[0077] The data acquisition module 1 of the object to be evaluated is used to obtain the sample size of the object to be evaluated, preset evaluation index data and target evaluation variables. This module executes the method described in step S1 in Embodiment 1, which will not be repeated here.
[0078] The data operation function library construction module 2 is used to build a data operation function library for performing data processing on the preset evaluation index data; this module executes the method described in step S2 in Embodiment 1, which will not be repeated here .
[0079] The hierarchical model building module 3 is used to construct a hierarchical model with a preset depth, and determine the hierarchical node where the preset evaluation index data is located; this module executes the method described in ste...
Embodiment 3
[0086] An embodiment of the present invention provides a terminal, such as Figure 5As shown, it includes: at least one processor 401 , such as a CPU (Central Processing Unit, central processing unit), at least one communication interface 403 , memory 404 , and at least one communication bus 402 . Wherein, the communication bus 402 is used to realize connection and communication between these components. Wherein, the communication interface 403 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Ramdom Access Memory, volatile random access memory), or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory 404 may also be at least one storage device located away from the aforementioned processor 401 . The processor 401 may execute the method for determining...
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