Machine Learning for Developers

Logistic distribution

This distribution is similar to the normal distribution, but with the morphological difference of having a more elongated tail. The main importance of this distribution lies in its cumulative distribution function (CDF), which we will be using in the following chapters, and will certainly look familiar.

Let's first represent the base distribution by using the following code snippet:

    import matplotlib.pyplot as plt #Import the plot library     import numpy as np     mu=0.5     sigma=0.5     distro2 = np.random.logistic(mu, sigma, 10000)     plt.hist(distro2, 50, normed=True)     distro = np.random.normal(mu, sigma, 10000)     plt.hist(distro, 50, normed=True)     plt.show() 

Take a look at the following graph:

Logistic (red) vs Normal (blue) ...