The actual implementation is the latter, that Perceptron() is equivalent to
SGDClassifier(loss=”perceptron”, eta0=1, learning_rate=”constant”,
penalty=None). I agree that it the comment in the documentation that
training the Perceptron is "slightly faster" and that the resulting model
will be more sparse is not really true. yi_pred will be in the domain of
real numbers. It might be worth changing the linear model documentation.
On Mon, Feb 23, 2015 at 11:14 AM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> Hi all,
>
> I find the description of the perceptron classifier a little bit ambiguous
> and was wondering if it would be worthwhile to clarify it a little bit.
> What do you think?
>
>
> While browsing through the documentation at
> http://scikit-learn.org/stable/modules/linear_model.html I found the
> following paragraph about perceptrons:
>
> > The Perceptron is another simple algorithm suitable for large scale
> learning. By default:
> > • It does not require a learning rate.
> > • It is not regularized (penalized).
> > • It updates its model only on mistakes.
> > The last characteristic implies that the Perceptron is slightly faster
> to train than SGD with the hinge loss and that the resulting models are
> sparser.
>
>
> To me, it sounds like the "classic" Rosenblatt Perceptron update rule
>
> weights = weights + eta(yi - yi_pred)xi
>
> where yi_pred = sign(w^T.x) [yi_pred ele in {-1, 1 }]
>
>
> However, when I read the documentation on
> http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron
>
> > Perceptron and SGDClassifier share the same underlying implementation.
> In fact, Perceptron() is equivalent to SGDClassifier(loss=”perceptron”,
> eta0=1, learning_rate=”constant”, penalty=None).
>
> it sounds more like the slightly more modern online learning variant of
> gradient descent (i.e. stochastic gradient descent):
>
> weights = weights + eta(yi - yi_pred)xi
>
> where yi_pred = w^T.x [yi_pred ele Real]
>
>
>
> Best,
> Sebastian
>
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