scikit-learn Cookbook - Second Edition

How to do it...

1. First, we need to create some data. For example, let's simulate heights of both women and men. We'll use this example throughout this recipe. It's a simple example, but hopefully it will illustrate what we're trying to accomplish in an N-dimensional space, which is a little easier to visualize:

import numpy as np N = 1000in_m = 72in_w = 66s_m = 2s_w = s_mm = np.random.normal(in_m, s_m, N)w = np.random.normal(in_w, s_w, N)from matplotlib import pyplot as plt%matplotlib inlinef, ax = plt.subplots(figsize=(7, 5))ax.set_title("Histogram of Heights")ax.hist(m, alpha=.5, label="Men");ax.hist(w, alpha=.5, label="Women");ax.legend()

This is the output:

2. Next, we might be interested in subsampling the group, fitting the distribution, ...