I used SVM.SVC function to classify. But when I wanted to calculate the weighted and unweighted average accuracy I couldn't access the confusion matrix. Because of svm.SVC.score only provides a classifier accuracy percentage. How can I calculate WAR and UAR?

you can find part of my script below :


    scaler = StandardScaler()
    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) 



Instead of using the score method on your trained model, you should use the predict method.

You can then pass the results into the confusion matrix function from sklearn:

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)

There is also a nice function called plot_confusion_matrix:

from sklearn.metrics import plot_confusion_matrix

plot_confusion_matrix(svmObject, testXtr, testY)

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