I can confirm that the Perceptron is super non-robust and the result
varies widely with the ``n_iter`` parameter.
I am not super confident about the Perceptron, does it only converge for
separable data?
Scaling the data helps, but this is not really what I expected:
from sklearn.datasets import load_iris
from sklearn.linear_model import Perceptron
iris = load_iris()
X = iris.data[:, [0,2]]
y = iris.target
clf = Perceptron(shuffle=True, n_iter=100).fit(X, y)
clf.predict(X)
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
With scaling:
from sklearn.preprocessing import StandardScaler
X_scaled = StandardScaler().fit_transform(X)
clf = Perceptron(shuffle=True, n_iter=100).fit(X_scaled, y)
clf.predict(X_scaled)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2])
Is a perceptron without scaling really that bad?
If so, we should really think about adding standard scaling into the
SGDClassifier....
On 02/10/2015 06:29 PM, Andy wrote:
Hi.
You don't need the OneVsRest classifier for the Perceptron.
Perceptron has penalty=None, so changing alpha doesn't change
anything. Enlarging n_iter should help.
Can you give the full code to reproduce your plots?
Thanks.
Andy
On 02/10/2015 05:53 PM, Sebastian Raschka wrote:
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) 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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