파이썬 LinearSVC

LinearSVC

from sklearn.svm import LinearSVC
model = LinearSVC()

 

LinearSVC 파라미터

# penalty = {‘l1’, ‘l2’}, 기본값=’l2’
# loss =  {‘hinge’, ‘squared_hinge’}, 기본값=’squared_hinge’
# dual = bool, 기본값=True
# tol = float, 기본값=1e-4
# C = float, 기본값=1.0

 

# multi_class = {‘ovr’, ‘crammer_singer’}, 기본값=’ovr’

# class_weight = {dict, ‘balanced’}, 기본값=None

 

# fit_intercept = bool, 기본값=True
# intercept_scaling = float, 기본값=1
# verbose = int, 기본값=0
# random_state = {int, RandomState instance, None}, 기본값=None
# max_iter = int, 기본값=1000

model.fit(X_train, y_train['gender'])
print(model.score(new_X_train, new_y_train))
predict = model.predict_proba(X_val)
pred = predict[:, 1]
from sklearn.metrics import roc_auc_score
print(roc_auc_score(y_val, pred))