Sklearn多类分类器

Question

我已经编写了以下代码以从文件导入数据向量并测试SVM分类器的性能（使用Sklearn和Python）。

但是，分类器的性能低于任何其他分类器（例如，NNET在测试数据上具有98％的精度，但最多可提供92％）。根据我的经验，SVM应该为这种数据产生更好的结果。

我可能做错了吗？

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

如果默认情况下不执行多级分类，该如何进行多级分类？

P.S。我的数据是以下格式（最后一列是类）：

2,2,2,2,2,1,0
2,2,2,2,1,2,0
0,2,2,5,2,2,3
2,2,2,4,2,2,1
2,2,2,4,2,0,0
2,2,2,4,2,1,1
2,2,2,4,1,2,1
0,2,2,5,2,2,3

Answer 1

我很长一段时间后发现了这个问题，如果有人需要它。

问题在于数据导入功能不会使数据融合。如果数据以某种方式进行分类，那么您有使用一些数据训练分类器并使用完全不同的数据进行测试的风险。在NNET情况下，使用MATLAB自动随机数据。

def buildData(filename, featureCols, testRatio):
f = open(filename)
data = np.loadtxt(fname = f, delimiter = ',')
np.random.shuffle(data)    # randomize the order
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