Standard Deviation (or standard deviation) is the square root of the variance. Standard deviation characterizes the dispersion of a feature - the larger the standard deviation, the greater the dispersion of feature values.
$$\sigma=\sqrt{D(X)}=\sqrt{E(X-E(X))^{2}}$$
where,
E(X)— mean of the random variable X.
The standard deviation of a finite sequence of numbers can be expressed by the formula:
$$\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\bar{x})^{2}}$$
where,
x̄— mean of random variables xi.
The sample standard deviation can be expressed by the formula (Bessel's correction):
$$\sigma=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(x_{i}-\bar{x})^{2}}$$
where,
x̄— mean of random variables xi.