title: Weighting a machine learning prediction with a cost of a false negative
body: <p>I have a classifier that predicts whether or not a part will be bad. These parts are almost the same but vary only slightly. However the importance of every part is different. For e.g. one part is used in 5 different machines while the next is used in the manufacture of a 100. The second part clearly has an additional risk associated with it. I have a model that predicts whether or not a part will fail which is thresholded reasonably well. How do I incorporate this cost of error simply. I would not like to re-train the model and have only RF/SVM's available.</p>