Yes, apologies for the confusion I was reading the code wrong. yi_pred is
not real.
On Mon, Feb 23, 2015 at 6:35 PM, Andy <t3k...@gmail.com> wrote:
> So indeed in the perceptron update yi_pred is {-1, 1}, not real, in
> sklearn, right?
>
>
>
> On 02/23/2015 08:35 AM, Mathieu Blondel wrote:
>
> Rosenblatt's Perceptron is a special case of SGD, see:
>
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/tests/test_perceptron.py
>
> The perceptron loss leads to sparser weight vectors than the hinge loss
> in the sense that it updates the weight vector less aggressively (only on
> mistakes while the hinge loss updates the model if the prediction is not
> "good enough").
>
> Mathieu
>
> On Mon, Feb 23, 2015 at 7:14 PM, 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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