While accuracy gives us a general sense of how often our classification model is correct, it doesn't tell the whole story, especially when dealing with uneven class distributions or when the consequences of different types of errors vary significantly. We need metrics that provide more specific insights. One such metric is precision.

Imagine you've built an email spam filter. Accuracy tells you the overall percentage of emails classified correctly (spam as spam, not spam as not spam). But you might be particularly interested in this question: Of all the emails that the filter put into the spam folder, how many were actually spam? You wouldn't want your filter to aggressively label important emails as spam. This focus on the correctness of positive predictions is exactly what precision measures.

What Precision Measures

Precision answers the question: Out of all the instances the model predicted to be positive, what fraction were actually positive?

It focuses on the predictions your model made for the positive class. Think of it as a measure of exactness or quality. A high precision score means that when your model predicts an instance belongs to the positive class, it is very likely correct.

The Precision Formula

To calculate precision, we use the values from the confusion matrix, specifically True Positives ( $TP$ ) and False Positives ( $FP$ ):

True Positives ( $TP$ ): The number of positive instances correctly predicted as positive.
False Positives ( $FP$ ): The number of negative instances incorrectly predicted as positive (also known as a "Type I error").

The formula for precision is:

Precision = \frac{TP}{TP + FP}

Notice the denominator ( $TP + FP$ ) represents the total number of instances your model predicted as positive. Precision is the ratio of the correctly predicted positive instances ( $TP$ ) to the total number predicted as positive.

The components used to calculate precision. It focuses solely on the instances classified as positive by the model ( $TP + FP$ ).

Example: Spam Filter Calculation

Let's return to our spam filter example. Suppose after testing the filter on 1000 emails, we get the following confusion matrix:

	Predicted: Spam	Predicted: Not Spam	Total Actual
Actual: Spam	TP = 95	FN = 5	100
Actual: Not Spam	FP = 10	TN = 890	900
Total Predicted	105	895	1000

To calculate precision, we need $TP$ and $FP$ :

$TP = 95$ (The filter correctly identified 95 spam emails)
$FP = 10$ (The filter incorrectly labeled 10 non-spam emails as spam)

Now, apply the formula:

Precision = \frac{TP}{TP + FP} = \frac{95}{95 + 10} = \frac{95}{105} \approx 0.905

So, the precision of our spam filter is approximately 0.905 or 90.5%. This means that when the filter marks an email as spam, it is correct about 90.5% of the time.

When is High Precision Important?

High precision is particularly desirable when the cost of a False Positive ( $FP$ ) is high. Consider these scenarios:

Spam Filtering: As discussed, you want to avoid marking legitimate emails as spam ( $FP$ ). Missing an important work email or personal message because it was wrongly classified as spam can be problematic. High precision ensures that emails flagged as spam are very likely actually spam.
Medical Diagnosis (Confirming a Serious Condition): If a positive prediction leads to expensive, invasive, or high-risk treatments, you want to be very sure the prediction is correct. A False Positive (diagnosing a healthy patient with the condition) could lead to unnecessary harm and cost.
Search Engine Results: When you search for something specific, you prefer the top results to be highly relevant. Irrelevant results appearing at the top (False Positives in terms of relevance) lead to a poor user experience.
Fraud Detection (Alerts): While catching fraud is important, generating too many false alerts ( $FP$ ) can overwhelm investigators and annoy customers whose legitimate transactions are flagged.

In these cases, we want to minimize False Positives, which translates to maximizing precision.

Precision Isn't Everything

Precision gives us valuable information about the reliability of positive predictions, but it doesn't consider False Negatives ( $FN$ ), the positive instances that the model incorrectly classified as negative. In our spam example, $FN = 5$ , meaning 5 actual spam emails slipped through the filter into the inbox. If minimizing these missed positive instances is important, we need to look at another metric: Recall. We'll examine Recall in the next section.