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问题：我需要训练一个分类器（在matlab中）来分类多个信号噪声水平。Sklearn支持向量机与Matlab SVM
所以我在matlab中使用fitcecoc训练了一个多类SVM并获得了92％的精度。
然后，我在python中使用sklearn.svm.svc训练了多类SVM，但似乎是我摆弄了参数，我无法达到超过69％的准确度。
30％的数据被阻止并用于验证培训。混淆矩阵可以在下面看到。
因此，如果任何人有一定的经验或建议与svm.svc多类培训，并可以在我的代码中看到一个问题，或者有什么建议，将不胜感激。
Python代码：
import numpy as np
from sklearn import svm
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
#from sklearn import preprocessing
#### SET fitting parameters here
C = 100
gamma = 1e-8
#### SET WEIGHTS HERE
C0_Weight = 1*C
C1_weight = 1*C
C2_weight = 1*C
C3_weight = 1*C
C4_weight = 1*C
#####
X = np.genfromtxt('data/features.csv', delimiter=',')
Y = np.genfromtxt('data/targets.csv', delimiter=',')
print 'feature data is of size: ' + str(X.shape)
print 'target data is of size: ' + str(Y.shape)
# SPLIT X AND Y INTO TRAINING AND TEST SET
test_size = 0.3
X_train, x_test, Y_train, y_test = train_test_split(X, Y,
... test_size=test_size, random_state=0)
svc = svm.SVC(C=C,kernel='rbf', gamma=gamma, class_weight = {0:C0_Weight,
... 1:C1_weight, 2:C2_weight, 3:C3_weight, 4:C4_weight},cache_size = 1000)
svc.fit(X_train, Y_train)
scores = cross_val_score(svc, X_train, Y_train, cv=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Out = svc.predict(x_test)
np.savetxt("data/testPredictions.csv", Out, delimiter=",")
np.savetxt("data/testTargets.csv", y_test, delimiter=",")
# calculate accuracy in test data
Hits = 0
HitsOverlap = 0
for idx, val in enumerate(Out):
Hits += int(y_test[idx]==Out[idx])
HitsOverlap += int(y_test[idx]==Out[idx]) + int(y_test[idx]==
... (Out[idx]-1)) + int(y_test[idx]==(Out[idx]+1))
print "Accuracy in testset: ", Hits*100/(11595*test_size)
print "Accuracy in testset w. overlap: ", HitsOverlap*100/(11595*test_size)
那些好奇我是怎么得到的参数，他们被发现与GridSearchCV（并增加了精确度从40％〜69）
任何帮助或建议非常赞赏。