Question

我正在使用SKLearn的LinearSVC（LibLinear）进行简单的分类。

我不能直接重现预测值并获得与＆＃34; LinearSVC.predict＆＃34;相同的准确度。确实。

我做错了什么？以下代码是独立的，并突出了我的问题。

import scipy as sc
import numpy as np
from sklearn.svm import LinearSVC #liblinear
N=6000
m=500

D = sc.sparse.random(N,m, random_state = 1)
D.data *= 2
D.data -= 1
X = sc.sparse.csr_matrix(D)
y = (X.sum(axis = 1) > .0)*2-1.0 

x_train = X[:5000,:]
y_train = y[:5000,:]
x_test  = X[5000:,:]
y_test  = y[5000:,:]

clf = LinearSVC(C=.1, fit_intercept = False, loss= 'hinge')
clf.fit(x_train,np.array(y_train))

print "Direct prediction accuracy:\t",100-100*np.mean((np.sign(x_test*clf.coef_.T)!=y_test)+0.0) ,"%"
print "CLF prediction accuracy:\t",  100*clf.score(x_test,y_test),"%"

输出：

Direct prediction accuracy:     90.8 %
CLF prediction accuracy:        91.3 %

感谢您的帮助！

Answer 1

区别在于如何处理零，当使用np.sign时，结果中的零没有被分类为任何有效的类（1或-1，因为你有一个二元分类器）;另一方面， Classifier.predict 严格输出两个类;从np.sign(x_test*clf.coef_.T)到(np.where(x_test * clf.coef_.T > 0, 1, -1)的预测方法的微小变化将提供与内置预测方法完全相同的精确度：


print "Direct prediction accuracy:\t", 100-100*np.mean((np.where(x_test * clf.coef_.T > 0, 1, -1) != y_test)+0.0) ,"%"
print "CLF prediction accuracy:\t",  100*clf.score(x_test, y_test),"%"

# Direct prediction accuracy:   92.7 %
# CLF prediction accuracy:  92.7 %

使用sklearn的LinearSVC进行的预测结果不一致

1 个答案: