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問題：我需要訓練一個分類器（在matlab中）來分類多個信號噪聲水平。Sklearn支持向量機與Matlab SVM
所以我在matlab中使用fitcecoc訓練了一個多類SVM並獲得了92％的精度。
然後，我在python中使用sklearn.svm.svc訓練了多類SVM，但似乎是我擺弄了參數，我無法達到超過69％的準確度。
30％的數據被阻止並用於驗證培訓。混淆矩陣可以在下面看到。
因此，如果任何人有一定的經驗或建議與svm.svc多類培訓，並可以在我的代碼中看到一個問題，或者有什麼建議，將不勝感激。
Python代碼：
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)
那些好奇我是怎麼得到的參數，他們被發現與GridSearchCV（並增加了精確度從40％〜69）
任何幫助或建議非常讚賞。