Understanding ML Evaluation Metrics — Precision & Recall
The jargon of machine learning world is crucial to convey how well your model works
Touching base, quoting a ball park number, hitting it out of the park, it being a whole new ball game are all examples of the jargon that is borrowed from the world of baseball and heavily used (or misused in some cases) in the corporate world.
Machine learning world similarly uses a set of terms routinely, to specify how well the models are working. The question arises — why do we need anything other than the term accuracy? Accuracy is simply defined as
Now imagine that an oncologist, an expert in breast cancer, has 1000 patients. He decides to declare all his patients as free of cancer but then later on finds out that 3 of those actually did have cancer. For the doctor it still means an accuracy of 99.7% but for those 3 patients the results are very severe. This thus is the pitfall to using just accuracy as your success metric.
These kind of “naive” results are obtained when we encounter an imbalanced dataset. An imbalanced dataset is one which has too few examples…
