roc curve

roc.py

# To compute the ROC curve and the area under the curve.
from sklearn.metrics import roc_curve, roc_auc_score

false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train_2, y_scores)

def plot_roc_curve(fpr, tpr, label=None):
    plt.plot(fpr, tpr, linewidth=2, label=label)
    plt.plot([0,1], [0,1], 'k--')
    plt.axis([0,1,0,1])
    plt.xlabel("False Positive Rate", fontsize=16)
    plt.ylabel("True Positive Rate", fontsize=16)
    
# plotting the curve
plt.figure(figsize=(8, 6))
plot_roc_curve(false_positive_rate, true_positive_rate)

# calculating th area under the curve
roc_auc_score(y_train_2, y_scores_forest)