The baseline can be a training production window of data or a training/validation dataset. That is not necessarily the case with JS divergence. {\displaystyle D(P\parallel Q)} I'm using the Jensen-Shannon-Divergence to measure the similarity between two probability distributions. for more than two probability distributions. The binning strategies can be even bins, quintiles and complex mixes of strategies that ultimately affect JS divergence (stay tuned for a future write-up on binning strategy). I have another question that I was hoping someone could help me with. rev2023.4.21.43403. KL(Q || P): 1.401 nats, The Jensen-Shannon divergence, or JS divergence for short, is another way to quantify the difference (or similarity) between two probability distributions The Jensen-Shannon divergence is the average Kullback-Leibler divergence of \(X\) and \(Y\) from their mixture distribution, \(M\): where \(M\) is the mixture distribution as before, and \(Z\) is an indicator variable over \(X\) and \(Y\). Nielsen, F. On a Generalization of the JensenShannon Divergence and the JensenShannon Centroid. If we consider the divergence of the left and right side we find: If we make that concave function \(\Psi\) the Shannon entropy \(\H{}\), we get the Jensen-Shannon divergence. Axis along which the Jensen-Shannon distances are computed. Or did you mean that this line is weird full stop? Detect feature changes between training and production to catch problems ahead of performance dips, Detect prediction distribution shifts between two production periods as a proxy for performance changes (especially useful in delayed ground truth scenarios), Use drift as a signal for when to retrain and how often to retrain, Catch feature transformation issues or pipeline breaks, Detect default fallback values used erroneously, Find clusters of new data that are problematic for the model in unstructured data, Find anomalous clusters of data that are not in the training set. What differentiates living as mere roommates from living in a marriage-like relationship? functions - How to calculate Jensen-Shannon divergence? - Mathematica ) ( Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Jensen-Shannon divergence extends KL divergence to calculate a symmetrical score and distance measure of one probability distribution from another. Note that the scipy entropy call below is the Kullback-Leibler divergence. If you want calculate "jensen shannon divergence", you could use following code: from scipy.stats import entropy from numpy.linalg import norm import numpy as np def JSD (P, Q): _P = P / norm (P, ord=1) _Q = Q / norm (Q, ord=1) _M = 0.5 * (_P + _Q) return 0.5 * (entropy (_P, _M) + entropy (_Q, _M)) Asking for help, clarification, or responding to other answers. ) P By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Xu, P.; Melbourne, J.; Madiman, M. Infinity-Rnyi entropy power inequalities. ; Panos, C. Information entropy, information distances, and complexity in atoms. Z 1 Many thanks. What is the correct way to implement Jensen-Shannon Distance?

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