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How can I normalize mutual information between to real-valued random variables using Python or R? sklearn.metrics.normalized_mutual_info_score seems to work for only nominal data. Or how to interpret the unnormalized scores?

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Strictly speaking you cannot just do that as-is.

Mutual information requires knowledge of your data generating probability density function. Nominal data naturally defines such pdf with counts. Continuous data doesn't.

There are two ways to handle that: either assume some pdf, like something parametric (or obtained with kernel smoothing), or convert your data to nominal data (this can be done for example using bucketing).

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