True positive (TP)

Introduction

True positive (TP) is when the machine learning model correctly predicts the positive class. Machine learning classification is the process of classifying data into distinct classes. When assessing a model's performance, it is essential to assess its capacity for correctly predicting each data instance's class. One important evaluation metric used in binary classification is True Positive (TP), which is when positive examples is correctly classified as positive by the model.

Example

True Positive is when binary classification models correctly predict a positive class. Consider a binary classification problem where we need to predict if someone has or does not have a disease based on some medical tests. In this scenario, the positive class represents those with the disease, while the negative class represents those without. If our model correctly predicts someone has the disease, and they actually do, then this prediction is known as a True Positive.

Explain Like I'm 5 (ELI5)

Machine learning algorithms strive for true positives - like when you find the toy you were searching for.

Imagine playing a game where you must locate a hidden toy in your room. When you finally locate it, you are thrilled that you found what you were searching for - an affirmative moment as you have successfully located what you needed. This is an achievement in itself as proof that your efforts paid off!

Machine learning teaches computers how to recognize objects, like pictures of cats. A true positive is when the computer correctly determines that something is indeed a cat when it really is one.

Similar to when humans play "find the cat", wherein a computer can correctly identify when something in an image is indeed a cat. This is beneficial, as we want the machine to be able to correctly recognize when something is indeed a cat.