What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?

The following article helps me a lot

https://medium.com/swlh/how-to-remember-all-these-classification-concepts-forever-761c065be33

• accuracy: Double-A rule
• precision: Triple-P rule

I'll try and explain how I remember what recall is.

Definition: Recall = True positives/All real world positives. OR Recall = True positives/True Positives and False Negatives.

Imagine an automobile company that wants to recall some of its cars for a manufacturing defect (hard to imagine, right?). This company obviously wants to get in all the cars that have the issue. That's our denominator. The total number of faulty cars.

It may indeed get hold of all of them, by calling every single car it ever manufactured. So here, it's recall would be perfect, a value of 1. There cannot be a false negative (part two of the denominator) since we labelled everything as positive!

In this case, the owner is obviously a multi-billionaire who doesn't care about the cost of the *recall exercise.

But what if a corporate entity wanted to cut costs (again, just go with me on this) by getting only the faulty cars in. Well, then, they would want to figure out something like, let's only call in cars that were manufactured in January this year as they have the maximum chances of this problem.

This creates our false negatives, that is, cars that have the problem but do not meet the January criterion. Therefore the second part of the denominator (FN) now becomes non-zero, which then reduces the overall fraction.

Key takeaways - it is the false negatives that fiddle with the recall metric. The mnemonic, if you really need it, is that cars get recalled. Hope this helps, somewhat.

for further study see this link

https://newbiettn.github.io/2016/08/30/precision-recall-sensitivity-specificity/

I use the word TARP to remember the difference between accuracy and precision.

TARP: True=Accuracy, Relative=Precision.

Accuracy measures how close a measurement is to the TRUE value, as the standard/accepted value is the TRUTH.

Precision measures how close measurements are RELATIVE to each other, or how low the spread between various measurements is.

Accuracy is truth, precision is relativity.

Hope this helps.

I created an interactive confusion table to help me understand the difference between these terms: http://zyxue.github.io/2018/05/15/on-the-p-value.html#interactive-confusion-table. I post the link here in case someone may find it helpful, too.

For precision and recall, each is the true positive (TP) as the numerator divided by a different denominator.

• Precision: TP / Predicted positive
• Recall: TP / Real positive

Mnemonics neatly eliminate man’s only nemesis: insufficient cerebral storage.

There is SNOUT SPIN:

• A Sensitive test, when Negative rules OUT disease
• A Specific test, when Positive, rules IN a disease.

I imagine a pig spinning around in a centrifuge, perhaps in preparation for going into space, to help me remember this mnemonic. Humming the theme to Tail Spin with the words appropriately changed can help the musically inclined from a certain generation.

I am not aware of any others.

In the context of binary classification:

Accuracy - How many instances did the model label correctly?

Recall - How often was the model able to find positives?

Precision - How believable the model is when it says an instance is a positive?