When is precision important

For instance, suppose you want to buy an apple. There are apples at the store, and 10 of them are bad. For instance, while most shoppers won't have much benefit from more than 18 good apples, the store would like to have more than 18 apples to sell.

Thus, precision will be more important than recall when the cost of acting is high, but the cost of not acting is low. Since the cost of buying a bad apple is high, but the cost of passing up a particular good apple is low, precision is more important in that example.

Another examples would be hiring when there's a lot of similar candidates. Recall is more important than precision when the cost of acting is low, but the opportunity cost of passing up on a candidate is high. There's the spam example I gave earlier the cost of missing out on an email address isn't high, but the cost of sending out an email to someone who doesn't respond is even lower , and another example would be identifying candidates for the flu shot: give the flu shot to someone who doesn't need it, and it costs a few dollars, don't give it to someone who does need it, and they could die.

Because of this, health care plans will generally offer the flu shot to everyone, disregarding precision entirely. Although in some situations recall may be more important than precision or vice versa , you need both to get a more interpretable assessment. For instance, as noted by SmallChess, in the medical community, a false negative is usually more disastrous than a false positive for preliminary diagnoses. Therefore, one might consider recall to be a more important measurement.

Accumulation has a great answer on how you can come up with more examples explaining the importance of precision over recall and vice versa.

Most of the other answers make a compelling case for the importance of recall so I thought I'd give an example on the importance of precision. This is a completely hypothetical example but it makes the case. Let us say that a machine learning model is created to predict whether a certain day is a good day to launch satellites or not based on the weather. If the model accidentally predicts that a good day to launch satellites is bad false negative , we miss the chance to launch.

This is not such a big deal. However, if the model predicts that it is a good day, but it is actually a bad day to launch the satellites false positive then the satellites may be destroyed and the cost of damages will be in the billions. I had a tough time remembering the difference between precision and recall, until I came up with this mnemonic for myself:.

With a pregnancy test, the test manufacturer needs to be sure that a positive result means the woman is really pregnant. People might react to a positive test by suddenly getting married or buying a house if many consumers got false positives and suffered huge costs for no reason, the test manufacturer would lack customers. I got a false negative pregnancy test once, and it just meant it took a few more weeks before I found out I was pregnant Pun intended. Now picture a call center for insurance claims.

Most fraudulent claims are phoned in on Mondays, after the fraudsters connect with collaborators and craft their made-up stories "let's say the car was stolen" over the weekend.

A random classifier the black line achieves an AUC of 0. Recall: the ability of a classification model to identify all data points in a relevant class.

Precision: the ability of a classification model to return only the data points in a class. Area under the curve AUC : metric to calculate the overall performance of a classification model based on area under the ROC curve. Skewed Data. Our task will be to diagnose patients with a disease present in 50 percent of the general population.

We will assume a black-box model, where we put in information about patients and receive a score between zero and one. We can alter the threshold for labeling a patient as positive has the disease to maximize the classifier performance. We will evaluate thresholds from 0. Here are the classification outcomes at each threshold:.

First, we make the confusion matrix:. Then we calculate the true positive and false positive rates to find the y and x coordinates for the ROC curve. To make the entire ROC curve, we carry out this process at each threshold. As you might imagine, this is pretty tedious, so instead of doing it by hand, we use a language like Python to do it for us! The Jupyter Notebook with the calculations is on GitHub for anyone to see the implementation. The final ROC curve is below with the thresholds above the points.

Here we can see all the concepts come together! At a threshold of 1. As the threshold decreases, the recall increases because we identify more patients that have the disease.

However, as our recall increases, our precision decreases because, in addition to increasing the true positives, we increase the false positives. At a threshold of 0. We can move along the curve for a given model by changing the threshold and can select the threshold that maximizes the F1 score. To shift the entire curve, we would need to build a different model. A model with a curve to the left and above our blue curve would be a superior model because it would have higher precision and recall at each threshold.

Based on the F1 score, the overall best model occurs at a threshold of 0. If we wanted to emphasize precision or recall to a greater extent, we could choose the corresponding model that performs best on those measures. Here at Qualitetch, we provide the very best service possible to make sure that precision etched components are always high quality and always working as you need them to be.

With so many different components and parts required from our team including connectors and contacts, mesh, sieves, washers, aerials, and springs and blades, the importance of precision lies in our hands. When you drive a car, a motorbike, an aeroplane or even a seated lawnmower, your movements are determined by the use of an engine.

Engines are engineered for a specific purpose and each and every component plays an important role in ensuring this happens safely. This is especially important when it comes to vehicles carrying passengers. The human body is just as complex and intricate as any engine. The surgeons that work on the human body need to be precise and accurate with every movement as there may well be a life at stake. This means they rely heavily on the tools and instruments designed and manufactured for these needs such as blades, cutters, forceps, clamps and cannulas.

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