each point along the curve in figure - corresponds to possible threshold of the decision_function we can seefor examplethat we can achieve recall of at precision of about the black circle marks the point that corresponds to threshold of the default threshold for decision_function this point is the trade-off that is chosen when calling the predict method the closer curve stays to the upper-right cornerthe better the classifier point at the upper right means high precision and high recall for the same threshold the curve starts at the top-left cornercorresponding to very low thresholdclassifying everything as the positive class raising the threshold moves the curve toward higher precisionbut also lower recall raising the threshold more and morewe get to situation where most of the points classified as being positive are true positivesleading to very high precision but lower recall the more the model keeps recall high as precision goes upthe better looking at this particular curve bit morewe can see that with this model it is possible to get precision of up to around with very high recall if we want much higher precisionwe have to sacrifice lot of recall in other wordson the left the curve is relatively flatmeaning that recall does not go down lot when we require increased precision for precision greater than each gain in precision costs us lot of recall different classifiers can work well in different parts of the curve--that isat different operating points let' compare the svm we trained to random forest trained on the same dataset the randomforestclassifier doesn' have decision_functiononly predict_proba the precision_recall_curve function expects as its second argument certainty measure for the positive class (class )so we pass the probability of sample being class --that isrf predict_proba(x_test)[: the default threshold for predict_proba in binary classification is so this is the point we marked on the curve (see figure - ) model evaluation and improvement