Method and system for characterization diagnosis and explanation, model comparison and training sample collection facing black box model
A model and black box technology, applied in computational models, neural learning methods, biological neural network models, etc., can solve problems such as the inability to objectively and concisely explain the internal logic of the black box model, the complexity of the explanation, and the inability to explain the degree of interpretability.
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[0114] The first embodiment of the present invention relates to a method for explaining a black-box model, the process of which is as follows figure 1 As shown, the method includes the following steps:
[0115] In step 101: provide a black-box model to be explained.
[0116] Any black-box model with input and output can be used as the input original black-box model to be explained in the present invention, such as but not limited to convolutional neural network, deep neural network, etc. The present invention does not limit the internal structure of the black-box model, that is, the black-box model of the present invention can adopt various internal structures.
[0117] Afterwards, enter step 102: input a certain sample into the black-box model to be interpreted, the sample contains features of a certain dimension. The sample can be any data that fits the input of the black box model to be interpreted. Optionally, the sample is a tabular dataset, an image dataset, or a text...
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