First, let’s load the CIFAR-10 data into memory as 4 arrays: the training data/labels and the test data/labels.
Nearest Neighbor Classifier
- The nearest neighbor classifier will take a test image, compare it to ‘every single one’ of the training images, and predict the label of the closest training image.
- L1 distance and L2 distance
k - Nearest Neighbor Classifier
- 和 Nearest Neighbor Classifier 区别是什么：The idea is very simple: instead of finding the 'single closest image' in the training set, we will find the 'top k closest images', and have them vote on the label of the test image. In particular, when k = 1, we recover the Nearest Neighbor classifier. Intuitively, higher values of k have a smoothing effect that makes the classifier more resistant to outliers
