from sklearn.svm import SVC svc=SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None,random_state=None) svc.fit(x_train_pca,y_train) SV...
http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC class sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision...
degree:在使用多项式核时,指定多项式的次数,默认为3。 gamma:核系数。对于非线性核函数,gamma可以影响模型的复杂度。较小的gamma会形成较大的决策边界,较大的gamma则会致使过拟合。 coef0:控制多项式核和Sigmoid核中常数项的影响。 class_weight:指定类别的权重,能够解决类别不平衡问题。 verbose:是否开启详细输出,默...
clf = SVC(kernel = "linear" ,gamma="auto" ,cache_size = 5000 ,class_weight = {1:15} #注意,这里写的其实是,类别1:10,隐藏了类别0:1这个比例 ).fit(Xtrain, Ytrain) result = clf.predict(Xtest) score = clf.score(Xtest,Ytest) recall = recall_score(Ytest, result) auc = roc_auc_...
如果给的核函数参数是其他核函数,则会自动忽略该参数。 gamma:float参数 默认为auto 核函数系数,只对‘rbf’,‘poly’,‘sigmod’有效。 如果gamma为auto,代表其值为样本特征数的倒数,即1/n_features. coef0:float参数 默认为0.0 核函数中的独立项,只有对‘poly’和‘sigmod’核函数有用,是指其中的参数c ...
sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,Tol=0.001, cache_size200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None,random_state=None)参数:...
sklearn中SVC和SVR的参数说明 SVC 转载于：官⽅源码 sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True,probability=False, tol=0.001, cache_size=200, class_weight=None,verbose=False, max_iter=-1, decision_function_shape='ovr',random_state=None)...
sklearn.svm.SVC(C=1.0,kernel='rbf', degree=3, gamma='auto',coef0=0.0,shrinking=True,probability=False,tol=0.001,cache_size=200, class_weight=None,verbose=False,max_iter=-1,decision_function_shape=None,random_state=None) 参数: l C:C-SVC的惩罚参数C?默认值是1.0 ...
sklearn.svm.SVC(C=1.0,kernel='rbf',degree=3,gamma='auto',coef0=0.0,shrinking=True,probability=False, tol=0.001,cache_size=200,class_weight=None,verbose=False,max_iter=-1,decision_function_shape=None,random_state=None) 参数: l C:C-SVC的惩罚参数C?默认值是1.0 ...
sklearn.svm.SVC(C=1.0,kernel='rbf',degree=3,gamma='auto',coef0=0.0,shrinking=True,probability=False,tol=0.001,cache_size=200,class_weight=None,verbose=False,max_iter=-1,decision_function_shape=None,random_state=None) 参数: l C:C-SVC的惩罚参数C?默认值是1.0 ...