roc auc score plotting
import scikitplot as skplt import matplotlib.pyplot as plt y_true = # ground truth labels y_probas = # predicted probabilities generated by sklearn classifier skplt.metrics.plot_roc_curve(y_true, y_probas) plt.show()
roc auc score plotting
import scikitplot as skplt import matplotlib.pyplot as plt y_true = # ground truth labels y_probas = # predicted probabilities generated by sklearn classifier skplt.metrics.plot_roc_curve(y_true, y_probas) plt.show()
roc curve
#ROC curve code snippet from external source(Module notes) def draw_roc( actual, probs ): fpr, tpr, thresholds = metrics.roc_curve( actual, probs, drop_intermediate = False ) auc_score = metrics.roc_auc_score( actual, probs ) plt.figure(figsize=(5, 5)) plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score ) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate or [1 - True Negative Rate]') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic example') plt.legend(loc="lower right") plt.show() return None fpr, tpr, thresholds = metrics.roc_curve( y_train_pred_final.Converted, y_train_pred_final.Converted_prob, drop_intermediate = False ) draw_roc(y_train_pred_final.Converted, y_train_pred_final.Converted_prob)
roc curve
# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs. cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci']) from sklearn.metrics import confusion_matrix # TP = confusion[1,1] # true positive # TN = confusion[0,0] # true negatives # FP = confusion[0,1] # false positives # FN = confusion[1,0] # false negatives num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9] for i in num: cm1 = metrics.confusion_matrix(y_train_pred_final.Churn, y_train_pred_final[i] ) total1=sum(sum(cm1)) accuracy = (cm1[0,0]+cm1[1,1])/total1 speci = cm1[0,0]/(cm1[0,0]+cm1[0,1]) sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1]) cutoff_df.loc[i] =[ i ,accuracy,sensi,speci] print(cutoff_df)
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