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 Xspecies column and the Ysetosa/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?
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 theY
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.Here's a link to a working kaggle notebook: kaggle.com/interestedmike/iris-dat...