I added the two lines above, but I still get the same error message. "ValueError: could not convert string to float: 'setosa'"
I think this may be because the train_X data still has the species in text format. How does the fit function know the relationship between the X species column and the Y setosa/versicolor/virginica columns? Do I need to do one-hot encoding on the X data?
Could you post a full, working Python script somewhere so I can see how this is supposed to work?
Oh right! Take out the species in the features array. That should fix the "ValueError: could not convert string to float: 'setosa'"
Also, I've added the missing from sklearn.metrics import mean_absolute_error
for the mean_absolute_error function.
from sklearn.metrics import mean_absolute_error
Here's a link to a working kaggle notebook: kaggle.com/interestedmike/iris-dat...
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