model=svm.svc(kernel='linear',c=1,gamma=1)# there is various option associatedwithit,like changing kernel,gamma andCvalue.Will discuss more # about itinnext section.Train the model using the training sets and check score model.fit(X,y)model.score(X,y)#Predict Output predicted=model.predi...
svmmodel=svm.SVC( C=10,kernel="linear",decision_function_shape="ovr") svmmodel.fit(X_train,y_train) print("SVM训练模型评分:" +str(accuracy_score(y_train,svmmodel.predict(X_train))) print("SVM待测模型评分:" +str(accuracy_score(y_test,svmmodel.predict(X_test))) SVM训练模型评分:0.93...
model = SVC(class_weight='balanced') # 拟合模型 model.fit(X, y) # 生成网格点用于绘制决策边界 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, ...
model = SVC(kernel='linear', C=C) model.fit(X, y) axi.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') plot_SVC_decision_function(model, axi) axi.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], s=300, lw=1, facecolors='none') axi.set_tit...
Python中的支持向量机(Support Vector Machine,SVM):理论与实践 支持向量机(Support Vector Machine,SVM)是一种强大的监督学习算法,主要用于分类和回归问题。本文将深入讲解Python中的支持向量机,包括算法原理、核函数、超参数调优、软间隔与硬间隔、优缺点,以及使用代码示例演示SVM在实际问题中的应用。
def createSVMmodel(X,y): '''创建SVM模型 参数: X - 训练集的特征数据 y - 训练集的类别 返回值: clf - 创建的模型 ''' clf = None clf=svm.SVC() clf.fit(X,y) return clf #训练模型 SVM = createSVMmodel(train_X,train_y) #预测 ...
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=...
[:,2]) model=svm.SVC(C=1.0,kernel='linear').fit(X,Y) for i in range(0,500): if Y[i]==1: plt.scatter(X[i,0],X[i,1],c='r') else: plt.scatter(X[i,0],X[i,1],c='b') w=model.coef_ b=model.intercept_ plt.plot(X[:,0],-(w[0][0]/w[0][1])*X[:,0]-...
model = svm.OneClassSVM(kernel='rbf', nu=0.05, gamma = 1) model.fit(X_train) # Generate test data x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 ...