数据挖掘 - 无法访问 SVM.SVC.score Scikit-learn Python 中的混淆矩阵 - 吾爱随笔录

我使用 SVM.SVC 函数进行分类。但是当我想计算加权和未加权平均准确率时，我无法访问混淆矩阵。因为 svm.SVC.score 只提供分类器准确率百分比。如何计算 WAR 和 UAR？

您可以在下面找到我的部分脚本：

'''

    scaler = StandardScaler()
    scaler.fit(trainX)
    trainXsc = scaler.transform(trainX)
    testXsc = scaler.transform(testX)

    pca = KernelPCA(n_components=j, kernel="sigmoid", random_state=1)  

    pca.fit(trainXsc)     # fit pca kernel with train data

    trainXtr    = pca.transform(trainXsc) # transform FV with PCA and dimension reduction
    testXtr     = pca.transform(testXsc)

    svmObject   = svm.SVC(C=2.0, 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='ovo', random_state=None)
                                        # SVM Kernel Function

    svmObject.fit(trainXtr, trainY)      # train SVM kernel with train FV

    result = svmObject.score(testXtr, testY)

'''

from sklearn.metrics import confusion_matrix y_pred = svmObject.predict(X) cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight, labels=labels, normalize=normalize)