Three summaries are immediately interpretible on the scale of the response variable:
rmse()
is the root-mean-squared-error
mae()
is the mean absolute error
qae()
is quantiles of absolute error.
Other summaries have varying scales and interpretations:
mape()
mean absolute percentage error.
rsae()
is the relative sum of absolute errors.
mse()
is the mean-squared-error.
rsquare()
is the variance of the predictions divided by the
variance of the response.
mse(model, data) rmse(model, data) mae(model, data) rsquare(model, data) qae(model, data, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)) mape(model, data) rsae(model, data)
model | A model |
---|---|
data | The dataset |
probs | Numeric vector of probabilities |
#> [1] 8.697561rmse(mod, mtcars)#> [1] 2.949163rsquare(mod, mtcars)#> [1] 0.7528328mae(mod, mtcars)#> [1] 2.340642qae(mod, mtcars)#> 5% 25% 50% 75% 95% #> 0.1784985 1.0005640 2.0946199 3.2696108 6.1794815mape(mod, mtcars)#> [1] 0.1260733rsae(mod, mtcars)#> [1] 0.1165042