Compute model quality for a given dataset
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 |
mod <- lm(mpg ~ wt, data = mtcars) mse(mod, mtcars) rmse(mod, mtcars) rsquare(mod, mtcars) mae(mod, mtcars) qae(mod, mtcars) mape(mod, mtcars) rsae(mod, mtcars)
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