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The "black box theory" poses A further however substantial problem. Black box refers to some problem in which the algorithm or the process of manufacturing an output is entirely opaque, that means that even the coders with the algorithm are unable to audit the sample the machine extracted out in the data.In machine learning, although working with s

In unsupervised machine learning, k-implies clustering could be utilized to compress data by grouping comparable data points into clusters. This method simplifies dealing with comprehensive datasets that deficiency predefined labels and finds common use in fields for instance impression compression.[31], from day-to-day tasks to Daring and imaginat