cjlin1
commented
4 years ago Internally libsvm did 5-fold cv to decide decision values for getting prob models. So if the # of data is too small, it may not be able to give proper prob estimates..
On 2020-04-30 17:49, wyykak wrote:
from libsvm.svmutil import * y, x = [1, -1], [[1,0,0], [-1,0,0]] prob = svm_problem(y, x) param = svm_parameter('-t 0 -b 1') model = svm_train(prob, param) p_label, p_acc, p_val = svm_predict(y, x, model,'-b 1') print(p_val)
Output:
Accuracy = 0% (0/2) (classification) [[0.3333333338127693, 0.6666666661872307], [0.6666666661872306, 0.3333333338127694]]
This happens with all the interfaces.
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wyykak
from libsvm.svmutil import *y, x = [1, -1], [[1,0,0], [-1,0,0]]prob = svm_problem(y, x)param = svm_parameter('-t 0 -b 1')model = svm_train(prob, param)p_label, p_acc, p_val = svm_predict(y, x, model,'-b 1')print(p_val)Output:
This happens with all the interfaces.