npc_gzip

of. That is, the accuracy you get if the stars happen to align and you get the luckiest possible result. Well, not only is this creative new metric not described in the paper, it’s also not applied to the other algorithms. For example, I think a neural network is capable of achieving a “max” accuracy of 100%, if all the initial weights happen to perfectly encode both the training and test sets. But of course they just use standard training to give the numbers for those algorithms. [1] https://github.com/bazingagin/npc_gzip/issues/3

x2 = len(gzip.compress(x1x2.encode()) ncd = (Cx1x2 - min(Cx1,Cx2)) / max(Cx1, Cx2) distance_from_x1.append(ncd) sorted_idx = np.argsort(np.array(distance_from_x1)) top_k_class = training_set[sorted_idx[:k], 1] predict_class = max(set(top_k_class), key=top_k_class.count) Abstract: In this paper, we propose a non-parametric alternative to DNNs that’s easy, lightweight, and universal in text classification: a combi- nation of a simple compressor like gzip with a k-nearest-neighbor classifier. Without any training parameters, our method achieves re- sults that are competitive with non-pretrained deep learning methods on six in-distribution datasets. It even outperforms BERT on all five OOD datasets, including four low-resource lan- guages. Our method also excels in the few-shot setting, where labeled data are too scarce to train DNNs effectively. Code is available at https://github.com/bazingagin/npc_gzip Ah, this was already mentioned a few days ago in HN: 73 comments: “Gzip and KNN Outperforms Transformers on Text Classification” https://news.ycombinator.com/item?id=36707193 7 comments: “Plain old gzip+kNN outperforms BERT and other DNNs” https://news.ycombinator.com/item?id=36705472 7 comments: “A Parameter-Free Classification Method with Compressors” https://news.ycombinator.com/item?id=36707509