Learning to recognize handwritten digits with a K-nearest neighbors classifier

In this recipe, we will see how to recognize handwritten digits with a K-nearest neighbors (K-NN) classifier. This classifier is a simple but powerful model, well-adapted to complex, highly nonlinear datasets such as images. We will explain how it works later in this recipe.

How to do it...

1. We import the modules:

   >>> import numpy as np
       import sklearn
       import sklearn.datasets as ds
       import sklearn.model_selection as ms
       import sklearn.neighbors as nb
       import matplotlib.pyplot as plt
       %matplotlib inline

2. Let's load the digits dataset, part of the datasets module of scikit-learn. This dataset contains handwritten digits that have been manually labeled:

   >>> digits = ds.load_digits()
       X = digits.data
       y = digits.target
       print((X.min(), X.max()))
       print(X.shape)
   (0.0, 16.0)
   (1797, 64)

   In the matrix X, each row contains 8*8=64 pixels (in grayscale, values between 0 and 16). The...