lesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can seelesson 4 of 5 · overfitting and regularization you can see
Checkpoint
One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Reproduce the walkthrough's k experiment on fixed data (the train set has two noisy labels: the 2.0/1 and 2.5/0 points). Implement knn(k, x) (sort by distance, take k, majority vote with votes * 2 > k), then for k=1 and k=3 print the line f"k={k} train={tr:.0%} test={te:.0%} gap={tr - te:+.0%}". Finish with a line reporting whether the bigger k shrank the gap.