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# multi-class classification | |
from sklearn.multiclass import OneVsRestClassifier | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import roc_curve | |
from sklearn.metrics import roc_auc_score | |
# generate 2 class dataset | |
X, y = make_classification(n_samples=1000, n_classes=3, n_features=20, n_informative=3, random_state=42) | |
# split into train/test sets | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42) | |
# fit model | |
clf = OneVsRestClassifier(LogisticRegression()) | |
clf.fit(X_train, y_train) | |
pred = clf.predict(X_test) | |
pred_prob = clf.predict_proba(X_test) | |
# roc curve for classes | |
fpr = {} | |
tpr = {} | |
thresh ={} | |
n_class = 3 | |
for i in range(n_class): | |
fpr[i], tpr[i], thresh[i] = roc_curve(y_test, pred_prob[:,i], pos_label=i) | |
# plotting | |
plt.plot(fpr[0], tpr[0], linestyle='--',color='orange', label='Class 0 vs Rest') | |
plt.plot(fpr[1], tpr[1], linestyle='--',color='green', label='Class 1 vs Rest') | |
plt.plot(fpr[2], tpr[2], linestyle='--',color='blue', label='Class 2 vs Rest') | |
plt.title('Multiclass ROC curve') | |
plt.xlabel('False Positive Rate') | |
plt.ylabel('True Positive rate') | |
plt.legend(loc='best') | |
plt.savefig('Multiclass ROC',dpi=300); |
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