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Chapter 5
Setting the threshold at 0.63, we see that we can still achieve a precision of above
80 percent, detecting good answers when we accept a low recall of 37 percent. This
means that we will detect only one in three bad answers, but those answers that we
manage to detect we would be reasonably sure of.
To apply this threshold in the prediction process, we have to use predict_
proba(), which returns per class probabilities, instead of predict(), which
returns the class itself:
>>> thresh80 = threshold[idx80][0]
>>> probs_for_good = clf.predict_proba(answer_features)[:,1]
>>> answer_class = probs_for_good>thresh80
We can confirm that we are in the desired precision/recall range using
classification_report:
>>> from sklearn.metrics import classification_report
>>> print(classification_report(y_test, clf.predict_proba [:,1]>0.63,
target_names=['not accepted', 'accepted']))
precision recall f1-score support
not accepted 0.63 0.93 0.75 108
accepted 0.80 0.36 0.50 92
avg / total 0.71 0.67 0.63 200
Using the threshold will not guarantee that we are always above the
precision and recall values that we determined previously together
with its threshold.
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