svm和logistic回归在python中的准确性差异

Question

I have two classifier in python such as svm and logistic regression.

from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn import svm

scaler = preprocessing.StandardScaler()
scaler.fit(synthetic_data)
synthetic_data = scaler.transform(synthetic_data)
test_data = scaler.transform(test_data)

svc = svm.SVC(tol=0.0001, C=100.0).fit(synthetic_data, synthetic_label)
predictedSVM = svc.predict(test_data)
print(accuracy_score(test_label, predictedSVM))

LRmodel = LogisticRegression(penalty='l2', tol=0.0001, C=100.0, random_state=1,max_iter=1000, n_jobs=-1)
predictedLR = LRmodel.fit(synthetic_data, synthetic_label).predict(test_data)
print(accuracy_score(test_label, predictedLR))

I use same input but their accuracy is so different. svm sometimes predicts all predicted svm as 1. Accuracy of svm is 0.45 and accuracy of logistic regression is 0.75. I changed parameters of C in a different ways, but I have still some problems.

Answer 1

It is because SVC by default uses radial kernel ( http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html ), which is something different than linear classification.

If you want to use linear kernel add parameter kernel='linear' to SVC.

If you want to keep using radial kernel, I suggest to also change gamma parameter.