MetricsWeighted: performance – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

performance

Performance

Description

Applies one or more metrics to a data.frame containing columns with actual and predicted values as well as an optional column with case weights. The results are returned as a data.frame and can be used in a dplyr chain.

Usage

performance(
  data,
  actual,
  predicted,
  w = NULL,
  metrics = rmse,
  key = "metric",
  value = "value",
  ...
)

Arguments

`data`	A `data.frame` containing `actual`, `predicted` and possibly `w`.
`actual`	The column name in `data` referring to actual values.
`predicted`	The column name in `data` referring to predicted values.
`w`	The optional column name in `data` referring to case weights.
`metrics`	Either a function or a named list of functions. Each function represents a metric and has four arguments: observed, predicted, case weights and `...`. If not a named list but a single function, the name of the function is guessed by `deparse(substitute(...))`, which would not provide the actual name of the function if called within `lapply` etc. In such cases, you can pass a named list with one element, e.g. `list(rmse = rmse)`.
`key`	Name of the resulting column containing the name of the metric. Defaults to "metric".
`value`	Name of the resulting column with the value of the metric. Defaults to "value".
`...`	Further arguments passed to the metric functions, e.g. if the metric is "r_squared", you could pass the relevant deviance function as additional argument (see examples).

Value

Data frame with one row per metric and two columns: key and value.

Examples

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)))

## End(Not run)

MetricsWeighted

Weighted Metrics, Scoring Functions and Performance Measures for Machine Learning

v0.5.2

GPL (>= 2)

Authors

Michael Mayer [aut, cre, cph], Christian Lorentzen [ctb, rev]

Initial release

2021-04-16