我有一组4个主成分（见下面的pc1:pc2），我用它们作为输入变量来预测我的y变量（下面的y-var）。我尝试使用SVM来预测y-var，方法如下，使用pc1和pc2作为输入：

from sklearn.decomposition import PCAfrom mlxtend.plotting import plot_decision_regionsfrom mlxtend.plotting import plot_decision_regionsfrom sklearn.svm import SVCdf = x_var[['pc1','pc2']].join(y_var["y-var"])clf = SVC(C=1,gamma=0.0001)X_train = np.array(df[['pc1', 'pc2']])y_train = np.array(df["y-var"])clf.fit(X_train, y_train)plot_decision_regions((X_train), (y_train), clf=clf, legend=2)

这给了我以下结果：

显然，SVM将所有内容都分类为’1’（在图片中看不到决策边界）。为什么我没有得到0和1的分类？我还尝试了不同的核函数，并进行了网格搜索，但结果总是相同的。

    pc1       pc2          y-var0   0.519179  0.247208      11   0.271661  0.378146      12   0.160372  0.395769      13   0.131858  0.377220      04  -0.082872  0.099886      15  -0.018304  0.125293      16  -0.075480  0.129186      17  -0.120394  0.103077      18  -0.079285  0.315473      09  -0.061470  0.373005      110 -0.114704  0.318144      011 -0.036623  0.402758      012 -0.266696  0.102101      113 -0.304520 -0.044354      114 -0.341065 -0.091845      115 -0.335393 -0.158577      116 -0.294246 -0.172631      117 -0.112002  0.107467      018 -0.008648  0.039244      019 -0.016432 -0.011859      120  0.025505 -0.003516      021  0.065414 -0.144414      022  0.058254 -0.199284      123  0.080844 -0.227434      124  0.146013 -0.177407      025  0.072719 -0.215493      126  0.076515 -0.218327      127  0.073930 -0.205280      028  0.084932 -0.213145      129  0.127504 -0.119456      130  0.410069 -0.070637      031  0.444208 -0.054756      032  0.359892 -0.039921      133  0.351449  0.039005      134  0.340579 -0.061595      135  0.195910 -0.088828      136  0.169974  0.014353      137  0.168284 -0.034547      038  0.163418  0.009783      139  0.222996 -0.020889      040  0.131592  0.197540      141  0.035192  0.160503      142 -0.005788  0.010568      143 -0.146251 -0.078299      044 -0.165629 -0.054383      145 -0.157875 -0.065957      046 -0.144255 -0.038511      147 -0.115826 -0.080849      048 -0.145774 -0.064944      149 -0.218346 -0.008935      150 -0.154941 -0.066568      051 -0.173926 -0.109107      052 -0.191553 -0.059816      153 -0.209128 -0.118813      1

回答：

你的代码运行正常，C和gamma的值似乎是问题所在。在你原来的代码中使用clf = SVC(C=1000, gamma=5)并修改C和gamma为其他值应该会产生结果。

使用C=1000和gamma=5的输出：

代码测试：

from sklearn.decomposition import PCAfrom mlxtend.plotting import plot_decision_regionsfrom mlxtend.plotting import plot_decision_regionsfrom sklearn.svm import SVCpc1 = [-0.114704, -0.036623, -0.266696, -0.304520]pc2 = [0.318144, 0.402758, 0.102101, -0.044354]yvar = [0, 0, 1, 1]import numpy as np    df = np.column_stack((pc1, pc2))clf = SVC(C=1, gamma=0.0001, kernel='linear')X_train = np.array(df)y_train = np.array(yvar)clf.fit(X_train, y_train)plot_decision_regions((X_train), (y_train), clf=clf, legend=2)

输出：

乘以一个较大的数字，

from sklearn.decomposition import PCAfrom mlxtend.plotting import plot_decision_regionsfrom mlxtend.plotting import plot_decision_regionsfrom sklearn.svm import SVCpc1 = [0.519179,0.271661,0.160372,0.131858,-0.082872,-0.018304,-0.075480,-0.120394,-0.079285,-0.061470,-0.114704,-0.036623,-0.266696,-0.304520,-0.341065,-0.335393,-0.294246,-0.112002,-0.008648,-0.016432,0.025505,0.065414,0.058254,0.080844,0.146013,0.072719,0.076515,0.073930,0.084932,0.127504,0.410069,0.444208,0.359892,0.351449,0.340579,0.195910,0.169974,0.168284,0.163418,0.222996,0.131592,0.035192,-0.005788,-0.146251,-0.165629,-0.157875,-0.144255,-0.115826,-0.145774,-0.218346,-0.154941,-0.173926,-0.191553,-0.209128]pc2 = [0.247208,0.378146,0.395769,0.377220,0.099886,0.125293,0.129186,0.103077,0.315473,0.373005,0.318144,0.402758,0.102101,-0.044354,-0.091845,-0.158577,-0.172631,0.107467,0.039244,-0.011859,-0.003516,-0.144414,-0.199284,-0.227434,-0.177407,-0.215493,-0.218327,-0.205280,-0.213145,-0.119456,-0.070637,-0.054756,-0.039921,0.039005,-0.061595,-0.088828,0.014353,-0.034547,0.009783,-0.020889,0.197540,0.160503,0.010568,-0.078299,-0.054383,-0.065957,-0.038511,-0.080849,-0.064944,-0.008935,-0.066568,-0.109107,-0.059816,-0.118813]yvar = [1,1,1,0,1,1,1,1,0,1,0,0,1,1,1,1,1,0,0,1,0,0,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,0,1,0,1,1,1,0,1,0,1,0,1,1,0,0,1,1]pc1 = [i * 10 for i in pc1]pc2 = [i * 10 for i in pc2]import numpy as npdf = np.column_stack((pc1, pc2))#df = x_var[['pc1','pc2']].join(y_var["y-var"])from sklearn.neural_network import MLPClassifierfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.svm import SVCfrom sklearn.gaussian_process import GaussianProcessClassifierfrom sklearn.gaussian_process.kernels import RBFfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifierfrom sklearn.naive_bayes import GaussianNBfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis#clf = RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)#clf = AdaBoostClassifier()#clf = QuadraticDiscriminantAnalysis()#clf = KNeighborsClassifier(3)#clf = DecisionTreeClassifier(max_depth=20)#clf = SVC(C=1, gamma=0.25)clf = SVC(C=100, gamma=0.5)X_train = np.array(df)y_train = np.array(yvar)clf.fit(X_train, y_train)plot_decision_regions((X_train), (y_train), clf=clf, legend=2)

输出：