Accuracy

In machine learning, accuracy refers to the ability to accurately predict the output of a given input. It is a common metric used to evaluate the performance of a model in particular classification tasks.

Mathematical Definition

The accuracy of a model in a classification task is the sum of all the correct predictions divided by the total number of predictions. It can be expressed mathematically as:

Accuracy = Number correct predictions / Total number of predictions

Example

Consider a binary classification problem, where the goal is to predict whether an email is spam email or not. In a set of 1000 emails, 800 are classified as "not spam" while 200 are classified as "spam". The model makes a total of 1000 predictions during the evaluation phase. The model correctly predicted that 750 emails were "not spam" while 150 emails were "spam"; 900 total correct predictions and 100 incorrect predictions. The accuracy of the model can thus be calculated as follows:

Accuracy = (750 + 150) / 1000 = 0.90

This means that the model accurately predicts the class of 90% percent of emails.

Explain Like I'm 5 (ELI5)

Accuracy can be described as a score on a test. Imagine that you have to answer 10 questions. If you get 9 correct answers, your accuracy score is 9/10 or 90%. This is how machine learning models can be tested. They are given questions and then based on how many answers they got correct, we calculate their accuracy score.