仅用一行代码：

适合SVM模型：

from sklearn import svm
svm = svm.SVC(gamma=0.001, C=100., kernel = 'linear')

并按以下方式实现图：

pd.Series(abs(svm.coef_[0]), index=features.columns).nlargest(10).plot(kind='barh')

结果将是：

以绝对值表示的SVM模型中最具贡献的功能

是的，SVM分类器具有属性coef_ ，但仅适用于具有线性内核的 SVM。对于其他内核，这是不可能的，因为数据是通过内核方法转换到与输入空间无关的另一个空间的，请查看说明 。

from matplotlib import pyplot as plt
from sklearn import svm

def f_importances(coef, names):
    imp = coef
    imp,names = zip(*sorted(zip(imp,names)))
    plt.barh(range(len(names)), imp, align='center')
    plt.yticks(range(len(names)), names)
    plt.show()

features_names = ['input1', 'input2']
svm = svm.SVC(kernel='linear')
svm.fit(X, Y)
f_importances(svm.coef_, features_names)

函数的输出如下所示：

我基于Jakub Macina的代码段创建了一个也适用于Python 3的解决方案。

from matplotlib import pyplot as plt
from sklearn import svm

def f_importances(coef, names, top=-1):
    imp = coef
    imp, names = zip(*sorted(list(zip(imp, names))))

    # Show all features
    if top == -1:
        top = len(names)

    plt.barh(range(top), imp[::-1][0:top], align='center')
    plt.yticks(range(top), names[::-1][0:top])
    plt.show()

# whatever your features are called
features_names = ['input1', 'input2', ...] 
svm = svm.SVC(kernel='linear')
svm.fit(X_train, y_train)

# Specify your top n features you want to visualize.
# You can also discard the abs() function 
# if you are interested in negative contribution of features
f_importances(abs(clf.coef_[0]), feature_names, top=10)