Hands-On Unsupervised Learning with Python

Anomaly detection

Let's now apply the Epanechnikov density estimation to perform an example of anomaly detection. According to the structure of the probability density, we have decided to impose a cut-off at p(x) < 0.005. Such a condition is displayed in the following screenshot:

Epanechnikov density estimation with anomaly cut-off

The red dots indicate the age limits for a sample to be classified as an anomaly. Let's compute the probability densities for some test points:

import numpy as nptest_data = np.array([12, 15, 18, 20, 25, 30, 40, 50, 55, 60, 65, 70, 75, 80, 85, 90]).reshape(-1, 1)test_densities_epanechnikov = np.exp(kd_epanechnikov.score_samples(test_data)) ...