Hi, all,
I have a question about the multiclass perceptron in scikit-learn. I noticed,
that I only get one decision boundary (http://i.imgur.com/CfyxPbt.png
<http://i.imgur.com/CfyxPbt.png>) in a simple 3 class setting.
iris = datasets.load_iris()
X = iris.data[:, [0,2]]
y = iris.target
I tried the perceptron with and without the OneVsRestClassifier
e.g.,
OneVsRestClassifier(estimator=Perceptron(alpha=1e-05, class_weight=None,
eta0=1.0, fit_intercept=True,
n_iter=20, n_jobs=1, penalty=None, random_state=123, shuffle=False,
verbose=0, warm_start=False),
n_jobs=1)
I also tried GridSearch on the alpha and n_iter parameter space. However, the
results didn't improve. Now, I am wondering why that is. Shouldn't the
multiclass perceptron produce something similar (but worse) like the linear
SVM? When the weights are updated at each iteration, shouldn't there be a
second hyperplane somewhat separating the green and red class (in the figure)
-- due to minimizing the cost function (i.e., minimizing number of
misclassifications) ?
Thanks,
Sebastian ------------------------------------------------------------------------------
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