您可以使用 sigmoid 函数 将 SVM 决策值转换为介于 0 和 1 之间的数字，可以视为概率。您可以根据您的数据调整参数和$f(d) = \frac{1}{1 + e^{-\alpha(d-\beta)}}$$d = (w, x) + b$$\alpha$$\beta$

有关更详细的方法，请参阅这些论文：

• B.Zadrozny, C. Elkan，将分类器分数转换为准确的多类概率估计。
• J.Drish，从支持向量机获得校准的概率估计。

至少在 R 中，只有两种算法提供了 LiblineaR 接口中的概率。

这是实际库的常见问题解答：

Q: How do I choose the solver? Should I use logistic regression or linear SVM? How about L1/L2 regularization?
Generally we recommend linear SVM as its training is faster and the accuracy is competitive. However, if you would like to have probability outputs, you may consider logistic regression.

Moreover, try L2 regularization first unless you need a sparse model. For most cases, L1 regularization does not give higher accuracy but may be slightly slower in training.

Among L2-regularized SVM solvers, try the default one (L2-loss SVC dual) first. If it is too slow, use the option -s 2 to solve the primal problem.

似乎 svm 算法没有提供概率作为输出。