Hey there! Today, I’m going to share with you a super cool conversation I had with ChatGPT about precision and recall. These are key concepts in the world of machine learning and text classification, and I found that generating examples with ChatGPT was a fantastic way to understand them better. So, let’s dive in!
PS.: This blog was generated by ChatGPT based on a conversation we have in portuguese using the plugins WebPilot and ImageSearch: https://chat.openai.com/share/b315fb29-841c-4f14-8939-a2657d1d5325. I only did some small adjustments.
Precision and Recall: The Basics
In text classification, precision and recall are two crucial metrics that help us understand how well our model is performing.
Precision is the proportion of positive predictions that were correctly classified. In other words, it’s the number of true positives (items correctly identified as belonging to the positive class) divided by the sum of true positives and false positives (items wrongly identified as belonging to the positive class). High precision means that when the model predicts an example is positive, it’s usually right.
Recall, also known as sensitivity, is the proportion of actual positive examples that were correctly identified. It’s the number of true positives divided by the sum of true positives and false negatives (items wrongly identified as belonging to the negative class). High recall means that the model correctly identifies most of the positive examples.
Both metrics are important and should be considered together. For instance, a model might have high precision but low recall if it’s very conservative and only makes positive predictions when it’s absolutely certain. On the other hand, a model might have high recall but low precision if it makes many positive predictions, including many that are incorrect. The F1 score, which is the harmonic mean of precision and recall, is often used to combine these two metrics into a single number.
Generating Examples with ChatGPT
To better understand these concepts, I asked ChatGPT to provide hypothetical examples illustrating high precision with low recall and vice versa. The examples were based on an email spam filtering system.
In the first scenario, the model was trained to be extremely cautious when classifying an email as spam. Out of 1000 emails, the model classified only 10 as spam, and all 10 were indeed spam. This resulted in a precision of 100%. However, there were actually 100 spam emails in the inbox, and the model only identified 10 of them, resulting in a recall of 10%.
In the second scenario, the model was trained to be aggressive in marking an email as spam. Out of 1000 emails, the model marked 500 as spam. All 100 actual spam emails were identified, but 400 non-spam emails were also marked as spam. This resulted in a precision of 20% and a recall of 100%.
Precision and Recall: A Deep Dive with Examples
Let’s take a closer look at the examples generated by ChatGPT to understand how precision and recall are calculated.
Example 1: High Precision, Low Recall
In this scenario, we have an email spam filtering system that is extremely cautious. Out of 1000 emails, it only classifies 10 as spam, and all 10 are indeed spam. There are actually 100 spam emails in the inbox, but the model only identifies 10 of them.
Here’s how we calculate precision and recall:
Precision: The number of true positives (TP) divided by the sum of true positives and false positives (FP). In this case, all 10 emails that were classified as spam were indeed spam, so TP = 10. There were no false positives, so FP = 0. Therefore, Precision = TP / (TP + FP) = 10 / (10 + 0) = 1, or 100%.
Recall: The number of true positives (TP) divided by the sum of true positives and false negatives (FN). In this case, TP = 10, and FN is the number of actual spam emails that were not identified as spam. Since there were 100 actual spam emails and the model only identified 10, FN = 100 - 10 = 90. Therefore, Recall = TP / (TP + FN) = 10 / (10 + 90) = 0.1, or 10%.
Example 2: Low Precision, High Recall
In this scenario, the model is aggressive in marking an email as spam. Out of 1000 emails, it marks 500 as spam. All 100 actual spam emails are identified, but 400 non-spam emails are also marked as spam.
Here’s how we calculate precision and recall:
Precision: TP = 100 (all the actual spam emails were identified), FP = 400 (non-spam emails that were incorrectly marked as spam). Therefore, Precision = TP / (TP + FP) = 100 / (100 + 400) = 0.2, or 20%.
Recall: TP = 100, FN = 0 (since all actual spam emails were identified). Therefore, Recall = TP / (TP + FN) = 100 / (100 + 0) = 1, or 100%.
These examples illustrate how a model can have high precision but low recall if it’s very conservative, and high recall but low precision if it’s very aggressive. Balancing these two metrics is a key challenge in machine learning.
The Confusion Matrix
The confusion matrix is a specific table layout that allows visualization of the classifications performed by an algorithm. It has two rows and two columns that report the number of false positives, false negatives, true positives, and true negatives. This allows more detailed analysis than mere proportion of correct classifications (accuracy).
Now you understand from where TP, FP, FN and TN come from!
Wrapping Up
I found this conversation with ChatGPT to be incredibly helpful in understanding precision and recall. The examples generated by ChatGPT were clear and easy to understand, and they really helped me grasp these concepts. I hope you found this as cool and helpful as I did!