CODEWITHSUNDEEP
pythonscikit-learnclassificationprecision-recall
Stanleyrr
Stanleyrr

Reputation: 875

How are 'positive' and 'negative' determined in Recall formula for machine learning?

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!

Upvotes: 0

Views: 854

Answers (1)

Kent Shikama
Kent Shikama

Reputation: 4060

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.

Upvotes: 2

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