)` + `\(x_{2}\)` + `\(x_{3}\)`… `\(x_{n}\)`

`\(y\)` is the dependent/response/target/outcome variable  
`\(x\)` are the independent/input/predictor/feature/covariate variables  

Supervised machine learning methods learn a target function `\(f\)` that best maps `\(x\)` to `\(y\)` based on a set of [training data](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#Training_dataset). 

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# Supervised machine learning

Our function would look like this: `\(y = f(x) + \epsilon\)` where `\(f