scikit-learn中的SVC和LinearSVC在什么参数下等效? [英] Under what parameters are SVC and LinearSVC in scikit-learn equivalent?
问题描述
我阅读了此线程关于scikit-learn中SVC()和LinearSVC()之间的差异.
I read this thread about the difference between SVC() and LinearSVC() in scikit-learn.
现在我有一个二进制分类问题的数据集(对于这样的问题，两个函数之间的一对一/一对一策略差异可以忽略.)
Now I have a data set of binary classification problem(For such a problem, the one-to-one/one-to-rest strategy difference between both functions could be ignore.)
我想尝试使用这两个函数为我提供相同结果的参数.首先，当然，我们应该将kernel='linear'设置为SVC()
但是，我无法从两个函数中获得相同的结果.我无法从文档中找到答案，有人可以帮助我找到我要寻找的等效参数集吗?
I want to try under what parameters would these 2 functions give me the same result. First of all, of course, we should set kernel='linear' for SVC()
However, I just could not get the same result from both functions. I could not find the answer from the documents, could anybody help me to find the equivalent parameter set I am looking for?
已更新: 我从scikit-learn网站的示例中修改了以下代码，显然它们并不相同:
Updated: I modified the following code from an example of the scikit-learn website, and apparently they are not the same:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
for i in range(len(y)):
if (y[i]==2):
y[i] = 1
h = .02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C, dual = True, loss = 'hinge').fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'LinearSVC (linear kernel)']
for i, clf in enumerate((svc, lin_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
plt.subplot(1, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
结果: 先前代码的输出图
推荐答案
从数学上讲，您需要设置:
In mathematical sense you need to set:
SVC(kernel='linear', **kwargs) # by default it uses RBF kernel
和
LinearSVC(loss='hinge', **kwargs) # by default it uses squared hinge loss
另一个无法轻易解决的元素是在LinearSVC中增加intercept_scaling，因为在此实现中偏差是有规律的(在SVC中不正确，在SVM中也不应该正确-因此这不是SVM )-因此，它们将从不完全相等(除非您的问题的偏倚= 0)，因为它们采用了两种不同的模型
Another element, which cannot be easily fixed is increasing intercept_scaling in LinearSVC, as in this implementation bias is regularized (which is not true in SVC nor should be true in SVM - thus this is not SVM) - consequently they will never be exactly equal (unless bias=0 for your problem), as they assume two different models
- SVC:
1/2||w||^2 + C SUM xi_i - LinearSVC:
1/2||[w b]||^2 + C SUM xi_i
- SVC :
1/2||w||^2 + C SUM xi_i - LinearSVC:
1/2||[w b]||^2 + C SUM xi_i
我个人认为LinearSVC是sklearn开发人员的错误之一-此类只是不是线性SVM .
Personally I consider LinearSVC one of the mistakes of sklearn developers - this class is simply not a linear SVM.
增加截距缩放比例(至10.0)
After increasing intercept scaling (to 10.0)
但是，如果您将其放大太多，它也会失败，因为现在容忍度和迭代次数至关重要.
However, if you scale it up too much - it will also fail, as now tolerance and number of iterations are crucial.
总结: LinearSVC不是线性SVM，请不要使用它.
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