In Python scikitlearn there's a function called 'sklearn.metrics.recall_score' (http://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html), which is calculated by TP/(TP+FN).
In my unsupervised classification model I am classifying emails into 2 classes: 1 or -1. So my question is how does the 'recall_score' function know whether label '1' is 'positive' or label '-1' is 'negative' since I didn't specify which is which? If the model treats '1' as positive, the result for recall will be different from if the model treats '-1' as positive.
Sorry if my question is not clear. Please let me know if you want me to provide any clarification. Thanks!
You need to specify the positive label. For example if you want -1 to be your positive label, you'll call
recall_score(y_true=[...], y_pred=[...], pos_label=-1)
Note by default 1 is the positive label.