Explain machine learning concepts in simple terms

Disclaimer

I use ChatGPT to create this ELI5 collection as a research into Human - Large Language Model collaboration. See methodology here.

I do not claim authorship nor guarantee the correctness of this article. However, I did put in efforts to make it correct and helpful in key aspects (see more in the methodology, link above).

less than 1 minute read

Linear regression is a type of machine learning model that is used to predict a numerical value based on one or more predictor variables. A predictor variable is a variable that is used to predict the value of the target variable, which is the variable that the model is trying to predict.

For example, imagine you are trying to predict how much a person weighs based on their height and age. In this case, height and age would be the predictor variables, and weight would be the target variable. You could use linear regression to find a line that represents the relationship between the predictor variables and the target variable. The line would help you make predictions about how much a person weighs based on their height and age.

Linear regression is useful when there is a clear linear relationship between the predictor variables and the target variable. If there is not a strong linear relationship, another type of model may be more appropriate.

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