The example labels in training set you give is a multiclass traininset
(one and only one class per sample). Use a multi-label set of training
labels to make the LabelBinarizer switch to the 1 hot encoding:
In [69]: y_train = (['New York', 'London'], ['London'])
In [70]: Y_indicator = LabelBinarizer().fit(y_train).transform(y_train)
In [71]: Y_indicator
Out[71]:
array([[ 1., 1.],
[ 1., 0.]])
------------------------------------------------------------------------------
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