Over the last few weeks, I’ve been working with some tricky features. Interestingly, I needed to add noise to the features to improve my classifier performance. I will write a post on these “confounding features” later. For now, let me just point out the following useful fact.

If

$$f(x, \sigma) = {1\over{\sigma \sqrt{2 \pi}}} \exp{\left(-{{x^2}\over{2 \sigma^2}}\right)},$$

then

$$\max_\sigma f(x,\sigma) = f(x, |x|).$$

So, if you have a Gaussian with mean zero and you want to fatten it to maximize the likelihood of the probability density function at $x$ without changing the mean, then set the standard deviation to $|x|$.

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