Distance Metrics Overview
Distance metrics are essential for measuring the similarity or dissimilarity between data points in various ML algorithms, including KNN. The choice of distance metric can significantly influence model performance, affecting how data points are classified and how clusters are formed so it’s best to get comfortable with more than the standard Euclidean distance most early data science practitioners default to. This recipe will give the reader an opportunity to compare how different distance metrics compare when datasets contain different properties.
Getting ready
We’ll create two new datasets for illustrating the differences between distance metrics using scikit-learn’s built-in make_circles() function.
Load libraries:
import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_circles from sklearn.model_selection import train_test_splitCreate two synthetic datasets that highlight metric differences:
n_samples ...
