I ran into this also, and decided it had to be some kind of typo with how the test set is set up. After a lot of head scratching, I think there’s a more subtle error with the setup of the test data. I believe when the species are assigned their positions in the one-hot encoding vector, that order is determined by the order in which the species are encountered in the test and training data.
For instance, if the species of the first 3 rows of the training set are "Iris-virginica", "Iris-setosa", ""Iris-versicolor", then those entries in the train_y data will look like [1, 0, 0], [0, 1, 0], [0, 0, 1] and the model will learn to predict [1, 0, 0] if the features match what it’s learned about “Iris-virginica.”
If the species are encountered in a different order in the test data, then we may end up with “Iris-virginica” having the 2nd position instead of the first in the test_y data, so the model will predict [1, 0, 0] but the scoring logic will be comparing against [0, 1, 0]
