# Precision Recall Curve Intepretation

I have made the Precision-Recall for my model. The red line is the prevalence. I do not understand the fluctuations in the beginning. Should it be more smooth?

This happens when the model has made false positive predictions with high confidence. As a simple example, assume we have 1000 test cases, of which 100 are positive and 900 are negative. Assume the 20 most confident predictions are as shown in this table (TP/FP/FN/TN/Recall/Precision columns calculated using cumulative predictions from the first row to the current row):

Confidence
Threshold
True
Label
TP FP FN TN Recall Precision
0.995 1 1 0 99 900 0.01 1.00
0.99 0 1 1 99 899 0.01 0.50
0.985 0 1 2 99 898 0.01 0.33
0.98 0 1 3 99 897 0.01 0.25
0.975 1 2 3 98 897 0.02 0.40
0.97 1 3 3 97 897 0.03 0.50
0.965 1 4 3 96 897 0.04 0.57
0.96 0 4 4 96 896 0.04 0.50
0.955 0 4 5 96 895 0.04 0.44
0.95 1 5 5 95 895 0.05 0.50
0.945 1 6 5 94 895 0.06 0.55
0.94 0 6 6 94 894 0.06 0.50
0.935 0 6 7 94 893 0.06 0.46
0.93 0 6 8 94 892 0.06 0.43
0.925 0 6 9 94 891 0.06 0.40
0.92 0 6 10 94 890 0.06 0.38
0.915 1 7 10 93 890 0.07 0.41
0.91 1 8 10 92 890 0.08 0.44
0.905 1 9 10 91 890 0.09 0.47
0.9 1 10 10 90 890 0.1 0.50

At the start of the table, when the threshold is high and so there are very few positive predictions, a single mis-classification makes a large difference to the precision. When the above is plotted as a PRC it looks like this: