ROC & AUC

28 Feb 2018 on Machine Learning

1. Confusion matrix

from sklearn.metrics import confusion_matrix

$ y_true = [2, 0, 2, 2, 0, 1]
$ y_pred = [0, 0, 2, 2, 0, 2]
$ confusion_matrix(y_true, y_pred)
array([[2, 0, 0],
       [0, 0, 1],
       [1, 0, 2]])

2. Score

- accuary

- precision

- recall

- fall-out

- F score

- precision vs recall

- classification report

$ from sklearn.metrics import *

$ y_true = [0, 1, 2, 2, 2]
$ y_pred = [0, 0, 2, 2, 1]
$ target_names = ['class 0', 'class 1', 'class 2']
$ print(classification_report(y_true, y_pred, target_names=target_names))

             precision    recall  f1-score   support

    class 0       0.50      1.00      0.67         1
    class 1       0.00      0.00      0.00         1
    class 2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5

3. ROC & AUC

- ROC (Receiver Operating Characteristic)

- AUC (Area Under the ROC Curve)

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Reference

ROC - wikipedia

precesion and recall - wikipedia