What Does Precision Mean?

Okay, so this brings us to the main point of the article. In our world, what is Precision? What relevance does all of the knowledge mentioned before have to it?

To put it simply, Precision is the ratio of all the Positives to the True Positives. That would be the proportion of patients that we accurately diagnose as having heart disease out of all those who genuinely do. That would be our issue statement. In terms of math:

Precision Calculus | Accuracy and Memory

What is our model’s precision? Yes, it is 0.843, meaning that it is accurate in predicting that a patient has heart disease 84% of the time.

What is Recall?

The recall is a metric that indicates how well our model finds True Positives. Recall therefore indicates the number of people who we accurately diagnosed as having heart disease out of all those who do. In terms of math:

Formula for Recall | Accuracy and Memory

Recall for our model is 0.86. Recall also provides an indicator of how well our model recognizes pertinent data. We call it True Positive Rate or Sensitivity. What happens if a patient has cardiac disease but is not treated because our model indicated that they should not? We want to stay out of a situation like that!

Examples

Precision:  This focuses on the accuracy of your spam identifications. It asks: “Out of all the emails your filter flagged as spam, how many were spam?”

Returning to the example, let’s say your filter flagged eight emails as spam out of 12. But upon checking, you find only 5 were indeed spam. In this case, precision is:

  • Precision = (Correctly Identified Spam) / (Total Emails Identified as Spam) = 5 / 8 = 62.5%

Your filter has a precision of 62.5%, which means that 62.5% of the emails it flagged as spam were actually spam. That’s not bad, but there’s more to the story.

  • Recall: This metric emphasizes capturing all the relevant emails, the actual spam. It asks: “Out of all the real spam emails, what proportion did your filter correctly identify?”

Let’s say 12 spam emails were lurking in your inbox. With your filter catching only 5, the recall is:

Recall = (Correctly Identified Spam) / (Total Actual Spam Emails) = 5 / 12 = 41.7%

The recall here is 41.7%, which indicates that your filter only caught 41.7% of the actual spam emails. It might be good at avoiding false positives (non-spam emails flagged as spam), but it’s missing a significant chunk of the real spam!

The Trade-Off:  Finding the perfect balance between precision and recall can be tricky. A high-precision filter might flag very few emails as spam to avoid mistakes, but it might miss important ones (low recall). On the other hand, a filter with high recall might catch most spam emails, but it might also flag some crucial emails as spam (low precision).

What to choose?

Selecting between Precision and Recall: Depending on the particular application, precision or recall may be more or less important. In a medical diagnosis system, for example:

  • Even if it results in some false positives (unnecessary testing), high recall may be essential for identifying as many positive instances (diseases) as feasible.
  • However, in order to prevent upsetting clients, a financial fraud detection system may place a higher priority on high precision—that is, limiting false positives, or transactions that are incorrectly refused.

You can efficiently assess your machine learning models and decide which measure is more important for your particular task by knowing the differences between precision and recall.

What Does Precision Mean?

Okay, so this brings us to the main point of the article. In our world, what is Precision? What relevance does all of the knowledge mentioned before have to it?

Simply put, Precision is the ratio of all the Positives to the True Positives. That would be the proportion of patients that we accurately diagnosed as having heart disease out of all those who genuinely do. That would be our issue statement. In terms of math:

Precision Calculus | Accuracy and Memory

What is our model’s precision? Yes, it is 0.843, meaning that it is accurate in predicting that a patient has heart disease 84% of the time.

FAQ’s 

Q: What is the difference between Precision and Recall in machine learning models?

A: Precision measures the accuracy of positive predictions (true positives out of all predicted positives), while Recall measures the model’s ability to find all actual positives (true positives out of all actual positives).

Q: How does a high Precision model differ from a high Recall model?

A: A high Precision model minimizes false positives, important in contexts like fraud detection. A high Recall model captures more true positives, crucial in areas like medical diagnosis to avoid missing true cases.

Q: What are some strategies to balance Precision and Recall?

A: Strategies include adjusting the decision threshold, using the F1 score to balance both metrics, performing cross-validation, and incorporating cost-sensitive learning to minimize the impact of false positives and negatives.

Q: Why is it important to understand the trade-off between Precision and Recall?

A: Understanding this trade-off helps tailor models to specific applications, ensuring effective performance. For instance, high Precision is crucial in fraud detection to avoid false positives, while high Recall is vital in medical diagnosis to ensure all true cases are identified.