classsklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', 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', break_ties=False, random_state=None) 可选参数 C:正则化参数。
def plot_svm(kernel, df_input, y, C, gamma, coef): svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig =...
np.arange(y_min-0.5, y_max+0.5, h)) def plot_svm(kernel, df_input, y, C, gamma, coef): svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])...
在分类任务中,SVM 构建一个决策边界,将不同类别的样本分开。SVC 类则实现了标准的支持向量机分类器。 SVC 类的构造函数有以下常用参数: sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, ...
svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig = px.scatter_3d(df, x='PCAz_1', y='PCAz_2', z=...
clf = svm.SVC(kernel='linear') clf.fit(X, y) # 绘制数据点 plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired) # 绘制决策边界ax = plt.gca() xlim = ax.get_xlim() ylim = ax.get_ylim() # 创建网格来评估模型
svc=SVC(kernel='poly',degree=2,gamma=1,coef0=0) svc.fit(X,Y) pre=svc.predict(T) print pre print svc.n_support_ print svc.support_ print svc.support_vectors_ 运行结果: [ 1 1 -1 -1] #预测结果 [2 3] #-1类和+1类分别有2个和3个支持向量 ...
svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig = px.scatter_3d(df, x='PCAz_1', y='PCAz_2', z=...
二、SVC相应参数的含义 python中SVC代码如下: sklearn.svm.SVC(*,C=1.0,kernel='rbf',degree=3,gamma='scale',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',break_ties=False,random_state=None) ...