What means true positives?

Hello! This is my first post ever and I am not an English speaker so I apologize in advance for any mistakes.

Let me start from a little introduce of myself. I’m Junior Frontend Developer at startup. In my spare time, I read about machine learning/data science and this is the topic of this post.

I was looking for answer to what are true positives, true negatives, false positives and false negatives. I found something with a little help of Google and chatGPT. I decided to write about it because I can better understand it and help you at the same time.

So I found that:
True positive - this means that the model has classified a given unit as positive and indeed it is positive
True negative - this means that the model has classified a given unit as negative, and indeed it is negative.
False positive - this means that the model has classified a given unit as positive, and indeed it is negative.
False negative - this means that the model has classified a given unit as negative, and indeed it is positive.

These concepts are used to evaluate the effectiveness of classification models and are important when interpreting the results. Many measures of model quality, such as accuracy, sensitivity and specificity, are based on these concepts.

But now you can ask: What does it mean that a given unit is positive or negative?

An entity can be described as negative or positive depending on whether it meets a given criterion

Good example is a model that classifies emails as spam or non-spam.
In this case, the unit (email) can be marked as positive if it is spam, or negative if it is not spam.

And this is it! I hope I helped you understand it better.
I would appreciate any feedback 🥳