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If instead our goal would have been to detect as much good or bad answers as
possible, we would be more interested in recall:
Chapter 5
The next screenshot shows all the good answers and the answers that have been
classified as being good ones:
In terms of the previous diagram, precision is the fraction of the intersection of
the right circle while recall is the fraction of the intersection of the left circle.
So, how can we optimize for precision? Up to now, we have always used 0.5 as
the threshold to decide whether an answer is good or not. What we can do now
is to count the number of TP, FP, and FN instances while varying that threshold
between 0 and 1. With these counts, we can then plot precision over recall.
The handy function precision_recall_curve() from the metrics module does
all the calculations for us as shown in the following code:
>>> from sklearn.metrics import precision_recall_curve
>>> precision, recall, thresholds = precision_recall_curve(y_test,
clf.predict(X_test)
[ 111 ]