<jupyter_start><jupyter_text>ROC CURVES<jupyter_code>from sklearn.metrics import roc_curve

y_score = model.fit(x_train, y_train).decision_function(x_test) #getting predictions on test data

false_alarms, hit_rate, thresholds = roc_curve(y_test, y_score) #passing the predictions and original target values to ROC function

fig, ax = plt.subplots(1, figsize=(12, 6))
plt.plot(false_alarms, hit_rate, color='blue', label='ROC curve for Breast Cancer Diagnostics')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel("False Alarms") #x-axis
plt.ylabel('Hit Rate') #y-axis
plt.legend(loc="lower right")
plt.show()
plt.waitforbuttonpress()<jupyter_output><empty_output>
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