precision and recall from confusion matrix python
from sklearn.metrics import confusion_matrix, plot_confusion_matrix
clf = # define your classifier (Decision Tree, Random Forest etc.)
clf.fit(X, y) # fit your classifier
# make predictions with your classifier
y_pred = clf.predict(X)
# get true negative (tn), false positive (fp)
# false negative (fn) and true positive (tp)
# from confusion matrix
M = confusion_matrix(y, y_pred)
tn, fp, fn, tp = M.ravel()
recall = tp / (tp + fn) # definition of recall
precision = tp / (tp + fp) # definition of precisionAre there any code examples left?
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