databricks · Certified-Machine-Learning-Professional · Q425 · multiple_choice · topic_1

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numer…

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

A.All of these reasons
B.JS is not normalized or smoothed
C.None of these reasons
D.JS is more robust when working with large datasets
E.JS does not require any manual threshold or cutoff determinations

Explanation

Selected Answer: E This is a key advantage of using Jensen-Shannon divergence. It produces a value between 0 and 1, which represents the divergence between two distributions. This value can be interpreted without needing to set arbitrary thresholds or cutoffs. In contrast, the KS test involves comparing the test statistic to a critical value, which can depend on the significance level chosen.

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