precision and recall from confusion matrix python

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Emmy
13 Jan 2019
1from sklearn.metrics import confusion_matrix, plot_confusion_matrix
2
3clf = # define your classifier (Decision Tree, Random Forest etc.)
4clf.fit(X, y) # fit your classifier
5
6# make predictions with your classifier
7y_pred = clf.predict(X) 
8        
9# get true negative (tn), false positive (fp)
10# false negative (fn) and true positive (tp) 
11# from confusion matrix
12M = confusion_matrix(y, y_pred)
13tn, fp, fn, tp = M.ravel() 
14
15recall = tp / (tp + fn)       # definition of recall
16precision = tp / (tp + fp)    # definition of precision
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