For ML learners and engineers checking how well a regression model fits. You get MAE, MSE, RMSE and R-squared from a simple list of actual and predicted pairs.
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You enter
3.0,2.5 / -0.5,0.0 / 2.0,2.1 / 7.0,7.8 / 4.2,3.9 / 5.5,6.0
The tool shows
n 6, MAE 0.45, MSE 0.2483, RMSE 0.4983, R2 0.9577
MAE averages the absolute errors, so every error counts equally. RMSE squares errors before averaging, so large misses weigh more; RMSE is always at least as large as MAE.
It means the model predicts worse than simply using the mean of the actual values. An R2 of 1 is a perfect fit and 0 is no better than the mean.
Report RMSE or MAE in the units of the target so people can judge the error size, and R2 to show how much variance the model explains.
Yes. It is free, needs no sign-up and runs entirely in your browser, so what you type is not uploaded. You only sign in if you want to email a result to yourself or save it to your CareerByteCode profile.
