Explain precision in the simplest terms
Question
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Answers ( 8 )
The model will output a confusion matrix which will consists of True Positives (TP), True Negatives (TN),
False Positives (FP) and False Negatives (FN).
True Positive – The model predicted the alarm rings and it actually rings
True Negative – The model predicted the alarm did not ring and it actually did not ring.
False Positive – The model predicted the alarm rings but it actually did not ring.
False Negative – The model predicted the alarm does not ring but it actually rings.
Precision = TP/(TP +FP) ——–> Out of the totally predicted positive, how many are really positive.
I will always find it confusing to remember these formulations. I am going to tell you one way through which you might not forget in your entire life. I read this method on some quora post.
Imagine that, your girlfriend gave you a birthday surprise every year in the last 10years. (Sorry, I didn’t intend to depress you if you don’t have one. Even I don’t have one)
However, one day, your girlfriend asks you:
‘Sweetie, do you remember all the birthday surprises from me?’
This simple question makes your life in danger.
To extend your life, you need to recall all 10 surprising events from your memory.
So, recall is the ratio of a number of events you can correctly recall a number of all correct events.
If you can recall all 10 events correctly, then, your recall ratio is 1.0 (100%). If you can recall 7 events correctly, your recall ratio is 0.7 (70%).
Now, it’s easier to map the word recall to real-life usage of that word.
However, you might be wrong in some answers.
For example, you answer 15 times, 10 events are correct and 5 events are wrong. This means you can recall all events but it’s not so precise.
So, precision is the ratio of a number of events you can correctly recall to a number of all events you recall (mix of correct and wrong recalls). In other words, it is how precise your recall.
From the previous example (10 real events, 15 answers: 10 correct answers, 5 wrong answers), you get 100% recall but your precision is only 66.67% (10 / 15).
Yes, you can guess what I’m going to say next. If a machine-learning algorithm is good at recall, it doesn’t mean that algorithm is good at precision. That’s why we also need an F1 score which is the (harmonic) mean of recall and precision to evaluate an algorithm.
You can refer to the formal definition from swaplaw007’s answer.
https://www.quora.com/What-is-the-best-way-to-understand-the-terms-precision-and-recall – This is the quora post link
Precision : Out of the predicted positive class by the model, how many are actually positive.
Precision= TP/TP+FP
where
TP:True positive (The positive class that is actually predicted positive by the model)
FP:False positive (The negative class that is actually predicted positive by the model)
PRECISION:
1) Precision attempts to answer the question: What proportion of positive identifications was actually correct?
precision = TP / (TP + FP)
2) A model that produces no false positives has a precision of 1.0
EXAMPLE: If we want to classify emails, Precision measures the percentage of emails flagged as spam that was correctly classified whereas Recall measures the percentage of actual spam emails that were correctly classified
Precision refers to what proportion of Positive identification is correct
Confusion Matrix consists of True Positive, True Negative, False Positive, False Negative.
Let say the alarm rings :- 1 True (True Positive)
Alarm doesn’t ring :- 0 False (False Positive)
Now,
True Positive : When predicted and actual value is 1 means The model predicts the alarm rings and it actually rings
True Negative : when predicted and actual value is 0 means the model predict the alarm doesn’t ring and it actually doesn’t
ring.
False Positive (Type 1 error): the model predicts the alarm rings but actually it doesn’t.
False Negative(Type 2 error): the model predicts the alarm doesn’t ring but actually it rings.
Precision = True Positive/True Positive+False Positive
Precision is calculated as the number of correct positive predictions divided by the total number of positive predictions. The best precision is 1.0, whereas the worst is 0.0.
Precision = TP/TP+FP
Precision =TP/TP+FP means when your model predicts positive how many times it is correct
Fire alarm prediction:
It could only have two outcomes. Either, it could ring or not ring. I will be categorising ring as 1 and not ring as 0.
True positive – It has been predicted that it will ring and actually it rings
True Negative – It has been predicted that it will not ring and in actual it did not ring
False positive – It has been predicted that it will ring but in actual it did not ring. It is also known as type 1 error.
False negative – It has been predicted that it will not ring but in actual it did ring. It is also known as type 2 error.
Precision(p): Out of all the positive classes we have predicted correctly, how many are actually positive. (tp/(tp+fp))