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...
- 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
- Let's load the
digitsdataset, part of the datasets module of scikit-learn. This dataset contains handwritten digits that have been manually labeled:
>>> digits = ds.load_digits() ...