我编写了以下代码从文件中导入数据向量并测试SVM分类器的性能(使用sklearn和python)。在
然而,分类器的性能比任何其他分类器都要低(例如,NNet在测试数据上的准确率为98%,但这最多只能达到92%)。根据我的经验,支持向量机对这类数据应该能产生更好的结果。在
我可能做错什么了吗?在
import numpy as np
def buildData(featureCols, testRatio):
f = open("car-eval-data-1.csv")
data = np.loadtxt(fname = f, delimiter = ',')
X = data[:, :featureCols] # select columns 0:featureCols-1
y = data[:, featureCols] # select column featureCols
n_points = y.size
print "Imported " + str(n_points) + " lines."
### split into train/test sets
split = int((1-testRatio) * n_points)
X_train = X[0:split,:]
X_test = X[split:,:]
y_train = y[0:split]
y_test = y[split:]
return X_train, y_train, X_test, y_test
def buildClassifier(features_train, labels_train):
from sklearn import svm
#clf = svm.SVC(kernel='linear',C=1.0, gamma=0.1)
#clf = svm.SVC(kernel='poly', degree=3,C=1.0, gamma=0.1)
clf = svm.SVC(kernel='rbf',C=1.0, gamma=0.1)
clf.fit(features_train, labels_train)
return clf
def checkAccuracy(clf, features, labels):
from sklearn.metrics import accuracy_score
pred = clf.predict(features)
accuracy = accuracy_score(pred, labels)
return accuracy
features_train, labels_train, features_test, labels_test = buildData(6, 0.3)
clf = buildClassifier(features_train, labels_train)
trainAccuracy = checkAccuracy(clf, features_train, labels_train)
testAccuracy = checkAccuracy(clf, features_test, labels_test)
print "Training Items: " + str(labels_train.size) + ", Test Items: " + str(labels_test.size)
print "Training Accuracy: " + str(trainAccuracy)
print "Test Accuracy: " + str(testAccuracy)
i = 0
while i < labels_test.size:
pred = clf.predict(features_test[i])
print "F(" + str(i) + ") : " + str(features_test[i]) + " label= " + str(labels_test[i]) + " pred= " + str(pred);
i = i + 1
如果默认情况下不进行多类别分类,怎么可能进行多类别分类?在
注:我的数据格式如下(最后一列为类):
^{pr2}$
我发现问题后很久,我把它张贴出来,以防有人需要它。在
问题是数据导入函数不会洗牌数据。如果数据是以某种方式排序的,那么就存在这样的风险:用一些数据训练分类器,然后用完全不同的数据测试它。在NNet的情况下,使用Matlab对输入数据进行自动洗牌。在
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