ROC for multiclass classification

As people mentioned in comments you have to convert your problem into binary by using OneVsAll approach, so you'll have n_class number of ROC curves.

A simple example:

from sklearn.metrics import roc_curve, aucfrom sklearn import datasetsfrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.svm import LinearSVCfrom sklearn.preprocessing import label_binarizefrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as pltiris = datasets.load_iris()X, y = iris.data, iris.targety = label_binarize(y, classes=[0,1,2])n_classes = 3# shuffle and split training and test setsX_train, X_test, y_train, y_test =\    train_test_split(X, y, test_size=0.33, random_state=0)# classifierclf = OneVsRestClassifier(LinearSVC(random_state=0))y_score = clf.fit(X_train, y_train).decision_function(X_test)# Compute ROC curve and ROC area for each classfpr = dict()tpr = dict()roc_auc = dict()for i in range(n_classes):    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])    roc_auc[i] = auc(fpr[i], tpr[i])# Plot of a ROC curve for a specific classfor i in range(n_classes):    plt.figure()    plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])    plt.plot([0, 1], [0, 1], 'k--')    plt.xlim([0.0, 1.0])    plt.ylim([0.0, 1.05])    plt.xlabel('False Positive Rate')    plt.ylabel('True Positive Rate')    plt.title('Receiver operating characteristic example')    plt.legend(loc="lower right")    plt.show()

This works for me and is nice if you want them on the same plot. It is similar to @omdv's answer but maybe a little more succinct.

def plot_multiclass_roc(clf, X_test, y_test, n_classes, figsize=(17, 6)):    y_score = clf.decision_function(X_test)    # structures    fpr = dict()    tpr = dict()    roc_auc = dict()    # calculate dummies once    y_test_dummies = pd.get_dummies(y_test, drop_first=False).values    for i in range(n_classes):        fpr[i], tpr[i], _ = roc_curve(y_test_dummies[:, i], y_score[:, i])        roc_auc[i] = auc(fpr[i], tpr[i])    # roc for each class    fig, ax = plt.subplots(figsize=figsize)    ax.plot([0, 1], [0, 1], 'k--')    ax.set_xlim([0.0, 1.0])    ax.set_ylim([0.0, 1.05])    ax.set_xlabel('False Positive Rate')    ax.set_ylabel('True Positive Rate')    ax.set_title('Receiver operating characteristic example')    for i in range(n_classes):        ax.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f) for label %i' % (roc_auc[i], i))    ax.legend(loc="best")    ax.grid(alpha=.4)    sns.despine()    plt.show()plot_multiclass_roc(full_pipeline, X_test, y_test, n_classes=16, figsize=(16, 10))