How big is mutual information? As the title states, mutual information has no upper bound. There seems to be no good way to compare absolute sizes. For example, the correlation coefficient can be subjectively selected as high as 0.9 or above. In the case of mutual information between information theory and probability statistics, the expression i (x; y) in Figure 1 (b) is defined by two random variables x and y, and can also be regarded as relative entropy. To a certain extent, it can be regarded as a measure of the distance between two random variables. The mutual information of two random variables is not. Calculation of mutual information (mutual information) Calculation of mutual information (mutual information) Let's use several sklearn functions as a simple example. The mutual_info_score function is used to calculate the mutual information between two discrete random variables. It can be used for.
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