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Can someone please explain why the decision boundary differs between multinomial (softmax) and One-vs-Rest Logistic Regression for multiclass classification. Example shown below

http://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py

I was under the wrong impression that both would yield the same decision boundary and it was just that the probabilities that softmax gives are normalized and interpretable.

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