现实工作中的数据集往往会出现不均衡的情况，正例和反例的比例可能是1:9，在这种情况下，分类器往往无法达到良好的性能。
此时模型的评估不能只用sklearn中自带的"score"函数，还需要使用精度和召回率来衡量模型的性能。
此时需要单独设置一个参数“class_weight”。
在sklearn官方文档的说明中，给出了使用示例：
说明地址：http://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py
print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm # we create clusters with 1000 and 100 points rng = np.random.RandomState(0) n_samples_1 = 1000 n_samples_2 = 100 X = np.r_[1.5 * rng.randn(n_samples_1, 2), 0.5 * rng.randn(n_samples_2, 2) + [2, 2]] y = [0] * (n_samples_1) + [1] * (n_samples_2) # fit the model and get the separating hyperplane clf = svm.SVC(kernel='linear', C=1.0) clf.fit(X, y) # fit the model and get the separating hyperplane using weighted classes wclf = svm.SVC(kernel='linear', class_weight={1: 10}) wclf.fit(X, y) # plot separating hyperplanes and samples plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors='k') plt.legend() # plot the decision functions for both classifiers ax = plt.gca() xlim = ax.get_xlim() ylim = ax.get_ylim() # create grid to evaluate model xx = np.linspace(xlim[0], xlim[1], 30) yy = np.linspace(ylim[0], ylim[1], 30) YY, XX = np.meshgrid(yy, xx) xy = np.vstack([XX.ravel(), YY.ravel()]).T # get the separating hyperplane Z = clf.decision_function(xy).reshape(XX.shape) # plot decision boundary and margins a = ax.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5, linestyles=['-']) # get the separating hyperplane for weighted classes Z = wclf.decision_function(xy).reshape(XX.shape) # plot decision boundary and margins for weighted classes b = ax.contour(XX, YY, Z, colors='r', levels=[0], alpha=0.5, linestyles=['-']) plt.legend([a.collections[0], b.collections[0]], ["non weighted", "weighted"], loc="upper right") plt.show()
类似的道理，在不平衡的数据集上，不管用何种算法，都可以对“class_weight”这个参数进行设定，提高模型的性能。