I am tying to plot an ROC curve for Binary classification using RandomForestClassifier
I have two numpy arrays one contains predicted values and one contains true values as follows:
In [84]: test
Out[84]: array([0, 1, 0, ..., 0, 1, 0])
In [85]: pred
Out[85]: array([0, 1, 0, ..., 1, 0, 0])
How do I port ROC curve and obtain AUC (Area Under Curve) for this binary classification result in ipython?
You need probabilities to create ROC curve.
In [84]: test
Out[84]: array([0, 1, 0, ..., 0, 1, 0])
In [85]: pred
Out[85]: array([0.1, 1, 0.3, ..., 0.6, 0.85, 0.2])
Example code from scikit-learn examples:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(2):
fpr[i], tpr[i], _ = roc_curve(test, pred)
roc_auc[i] = auc(fpr[i], tpr[i])
print roc_auc_score(test, pred)
plt.figure()
plt.plot(fpr[1], tpr[1])
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')
plt.show()
test or pred is not equal to 0. if it is use anyarray.reshape(-1) . you can obtain probabilities using model.predict_proba(testdata)[:, 1]
plt.plot(fpr[2], tpr[2]) I changed it to 1 ... Everything else worked !!!
fpr[2] in the example is because there were 3 classes. For binary classification, just compute fpr, tpr, _ = roc_curve(y_test, y_score) and plot x=fpr, y=tpr.