I am looking for the meaning of verbose log abbriviations of SVC function in scikit-learn?
If nSV is the number of support vectors, #iter is the number of iteration, what dose nBSV, rho,obj mean?
This is an example:
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)
and here is the result
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.
Excerpt:
Q: The output of training C-SVM is like the following. What do they mean?
optimization finished, #iter = 219
nu = 0.431030
obj = -100.877286, rho = 0.424632
nSV = 132, nBSV = 107
Total nSV = 132
obj is the optimal objective value of the dual SVM problem
rho is the bias term in the decision function sgn(w^Tx - rho)
nSV and nBSV are number of support vectors and bounded support vectors (i.e., alpha_i = C)
nu-svm is a somewhat equivalent form of C-SVM where C is replaced by nu
nu simply shows the corresponding parameter. More details are in libsvm document
