# NOT RUN {
ir <- iris
fit_num <- lm(Sepal.Length ~ ., data = ir)
ir$fitted <- fit_num$fitted
performance(ir, "Sepal.Length", "fitted")
performance(ir, "Sepal.Length", "fitted", metrics = r_squared)
performance(ir, "Sepal.Length", "fitted", metrics = c(`R-squared` = r_squared, rmse = rmse))
performance(ir, "Sepal.Length", "fitted", metrics = r_squared,
deviance_function = deviance_gamma)
performance(ir, "Sepal.Length", "fitted", metrics = r_squared,
deviance_function = deviance_tweedie)
performance(ir, "Sepal.Length", "fitted", metrics = r_squared,
deviance_function = deviance_tweedie, tweedie_p = 2)
performance(ir, "Sepal.Length", "fitted", metrics = r_squared,
deviance_function = deviance_tweedie, tweedie_p = 0)
# }
# NOT RUN {
library(dplyr)
iris %>%
mutate(pred = predict(fit_num, data = .)) %>%
performance("Sepal.Length", "pred")
# Same
iris %>%
mutate(pred = predict(fit_num, data = .)) %>%
performance("Sepal.Length", "pred", metrics = rmse)
# Grouped by Species
iris %>%
mutate(pred = predict(fit_num, data = .)) %>%
group_by(Species) %>%
do(performance(., "Sepal.Length", "pred"))
# Multiple measures
iris %>%
mutate(pred = predict(fit_num, data = .)) %>%
performance("Sepal.Length", "pred",
metrics = list(rmse = rmse, mae = mae, `R-squared` = r_squared))
# Grouped by Species
iris %>%
mutate(pred = predict(fit_num, data = .)) %>%
group_by(Species) %>%
do(performance(., "Sepal.Length", "pred",
metrics = list(rmse = rmse, mae = mae, `R-squared` = r_squared)))
# }
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