问题描述
我在 scikit-learn 中寻找 SVC 函数的详细日志缩写的含义?
I am looking for the meaning of verbose log abbriviations of SVC function in scikit-learn?
如果nSV是支持向量数，#iter是迭代次数，nBSV, rho,obj是什么意思?
If nSV is the number of support vectors, #iter is the number of iteration, what dose nBSV, rho,obj mean?
这是一个例子:
import numpy as np
from sklearn.svm import SVR
sets=np.loadtxt('data\Exp Rot.txt') # reading data
model=SVR(kernel='rbf',C=100,gamma=1,max_iter=100000,verbose=True)
model.fit(sets[:,:2],sets[:,2])
print(model.score)
这是结果
推荐答案
scikit-learn 正在使用 libsvm 支持向量机的实现(LinearSVC 将使用 liblinear 由相同的作者).官方网站有自己的常见问题解答，可以在这里回答这个问题.
scikit-learn is using libsvm's implementation of support-vector machines (LinearSVC will use liblinear by the same authors).The official website has it's own FAQ answering this here.
摘录:
Q:训练C-SVM的输出如下.它们是什么意思?
优化完成，#iter = 219
optimization finished, #iter = 219
nu = 0.431030
nu = 0.431030
obj = -100.877286，rho = 0.424632
obj = -100.877286, rho = 0.424632
nSV = 132，nBSV = 107
nSV = 132, nBSV = 107
总 nSV = 132
Total nSV = 132
obj 是对偶 SVM 问题的最优目标值
obj is the optimal objective value of the dual SVM problem
rho 是决策函数 sgn(w^Tx - rho) 中的偏差项
rho is the bias term in the decision function sgn(w^Tx - rho)
nSV 和 nBSV 是支持向量和有界支持向量的数量(即 alpha_i = C)
nSV and nBSV are number of support vectors and bounded support vectors (i.e., alpha_i = C)
nu-svm 是 C-SVM 的某种等效形式，其中 C 被 nu 替换
nu-svm is a somewhat equivalent form of C-SVM where C is replaced by nu
nu 只是显示相应的参数.更多细节在 libsvm 文档中
nu simply shows the corresponding parameter. More details are in libsvm document
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