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I am building 2 models.
Model 1
modelgb = GradientBoostingClassifier()
modelgb.fit(x_train,y_train)
predsgb = modelgb.predict_proba(x_test)[:,1]
metrics.roc_auc_score(y_test,predsgb, average='macro', sample_weight=None)
Model 2
model = LogisticRegression()
model = model.fit(x_train,y_train)
predslog = model.predict_proba(x_test)[:,1]
metrics.roc_auc_score(y_test,predslog, average='macro', sample_weight=None)
How do i plot both the ROC curves in one plot , with a legend & text of AUC scores for each model ?
621
AUC-ROC for Multi-Class Classification Like I said before, the AUC-ROC curve is only for binary classification problems. But we can extend it to multiclass classification problems by using the One vs All technique.
To plot the ROC curve, we need to calculate the TPR and FPR for many different thresholds (This step is included in all relevant libraries as scikit-learn ). For each threshold, we plot the FPR value in the x-axis and the TPR value in the y-axis. We then join the dots with a line. That's it!
Try adapting this to your data:
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
plt.figure(0).clf()
pred = np.random.rand(1000)
label = np.random.randint(2, size=1000)
fpr, tpr, thresh = metrics.roc_curve(label, pred)
auc = metrics.roc_auc_score(label, pred)
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
pred = np.random.rand(1000)
label = np.random.randint(2, size=1000)
fpr, tpr, thresh = metrics.roc_curve(label, pred)
auc = metrics.roc_auc_score(label, pred)
plt.plot(fpr,tpr,label="data 2, auc="+str(auc))
plt.legend(loc=0)
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