Chapter 3, different amount of misclassfied samples about python-machine-learning-book-2nd-edition HOT 1 CLOSED

I just double-checked and you are right. This difference occurs in scikit-learn 0.20. If I use 0.19, I still get the 3 examples misclassified. In any case, for the perceptron, this is not important as it does not converge anyway when the classes are not linearly seperable, so you will get different results depending on the random seed or the number of iterations. I.e., it will cyclye through 2-9 misclassifications if you run it longer.

In other words, it's not an issue in your code, more like a shortcoming of the perceptron algorithm. If you are interested, I have some more details about the perceptron algorithms in my lecture slides: https://github.com/rasbt/stat479-deep-learning-ss19/blob/master/L03_perceptron/L03_perceptron_slides.pdf

from python-machine-learning-book-2nd-edition.