Cluster-Based Prediction

This method is already baked into some of my most recent posts, but I wanted to call attention to it in isolation, because it is interesting and useful. Specifically, my algorithms are generally rooted in a handful of lemmas and corollaries that I introduced, that prove, that the nearest neighbor method produces perfect accuracy, when classifications don’t change over small fixed distances. That is, if I’m given a row vector x from the dataset, and the set of points near x have the same classifier as x, then the nearest neighbor algorithm can be modified slightly to produce perfect accuracy. And I’ve introduced a ton of software that allows you to find that appropriate distance, which I call \delta. The reason this sort of new approach (I came up with this a while ago) is interesting, is because it doesn’t require any supervision –

It uses a fixed form formula to calculate \delta, as opposed to a training step.

This results in a prediction algorithm that has O(N) runtime, and the accuracy is consistently better than nearest neighbor. Note that you can also construct an O(\log(N)) runtime algorithm using the code attached to my paper Sorting, Information, and Recursion.

Here are the results as applied to the MNIST Fashion Dataset using 7,500 randomly selected rows, run 500 times, on 500 randomly selected training / testing datasets:

1. Nearest Neighbor:

Accuracy: 87.805% (on average)

2. Cluster-Based:

Accuracy: 93.188% (on average)

Given 500 randomly selected training / testing datasets, the cluster-based method beat the accuracy of nearest neighbor method 464 times, the nearest neighbor method beat the cluster-based method 0 times, and they were equal 36 times. The runtime from start to finish is a few seconds (for a single round of predictions), including preprocessing the images, running on an iMac.

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