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Accuracy: Difference between revisions
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The accuracy of a model in a classification task is the sum of all the correct [[prediction]]s divided by the total number of predictions. It can be expressed mathematically as: | The accuracy of a model in a classification task is the sum of all the correct [[prediction]]s divided by the total number of predictions. It can be expressed mathematically as: | ||
Accuracy = Number correct predictions / Total number of predictions | |||
==Example== | ==Example== | ||
Consider a binary classification problem, where the goal is predict whether an email is spam or not. | 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. | Accuracy = (750 + 150) / 1000 = 0.90 | ||
This means that the model accurately predicts the class of 90% percent of emails. | This means that the model accurately predicts the class of 90% percent of emails. | ||
==Explain Like I'm 5 (ELI5)== | ==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. | 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. | ||