scorer
is a set of tools for quickly scoring models in data science and machine learning. This toolset is written in C++, where possible, for blazing fast performance. This toolset's API follows that of sklearn.metrics as closely as possible so one can easily switch back and forth between the two languages without too much cognitive dissonance. The following types of metrics are currently implemented in scorer
:
The following types of metrics are soon to be implemented in scorer
:
You can install:
the latest released version from CRAN with
r
install.packages("scorer")
the latest development version from Github with
r
if (packageVersion("devtools") < 1.6) {
install.packages("devtools")
}
devtools::install_github("paulhendricks/scorer")
If you encounter a clear bug, please file a minimal reproducible example on github.
mae()
.library("scorer")
packageVersion("scorer")
#> [1] '0.2.0'
data(mtcars)
library("ggplot2")
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = 'lm') +
expand_limits(x = c(0, 6), y = c(0, 40))
set.seed(1)
n_train <- floor(nrow(mtcars) * 0.60)
n_test <- nrow(mtcars) - n_train
mask <- sample(c(rep(x = TRUE, times = n_train), rep(x = FALSE, times = n_test)))
mtcars[, "Type"] <- ifelse(mask, "Train", "Test")
train_mtcars <- mtcars[mask, ]
test_mtcars <- mtcars[!mask, ]
ggplot(mtcars, aes(x = wt, y = mpg, color = Type)) +
geom_point() +
expand_limits(x = c(0, 6), y = c(0, 40))
model <- lm(mpg ~ wt, data = train_mtcars)
test_mtcars[, "predicted_mpg"] <- predict(model, newdata = test_mtcars)
scorer::mean_absolute_error(test_mtcars[, "mpg"], test_mtcars[, "predicted_mpg"])
#> [1] 3.287805
scorer::mean_squared_error(test_mtcars[, "mpg"], test_mtcars[, "predicted_mpg"])
#> [1] 15.43932
final_model <- lm(mpg ~ wt, data = mtcars)
mtcars[, "predicted_mpg"] <- predict(final_model, newdata = mtcars)
scorer::explained_variance_score(mtcars[, "mpg"], mtcars[, "predicted_mpg"])
#> [1] 847.7252
scorer::unexplained_variance_score(mtcars[, "mpg"], mtcars[, "predicted_mpg"])
#> [1] 278.3219
scorer::total_variance_score(mtcars[, "mpg"], mtcars[, "predicted_mpg"])
#> [1] 1126.047
scorer::r2_score(mtcars[, "mpg"], mtcars[, "predicted_mpg"])
#> [1] 0.7528328
# TO BE UPDATED
The original author of scorer
is @Paul Hendricks.
The lead maintainer of scorer
is @Paul Hendricks.
sessionInfo()
#> R version 3.2.3 (2015-12-10)
#> Platform: x86_64-apple-darwin13.4.0 (64-bit)
#> Running under: OS X 10.11.3 (El Capitan)
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_2.0.0 scorer_0.2.0
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_0.12.3 digest_0.6.9 plyr_1.8.3 grid_3.2.3
#> [5] gtable_0.1.2 formatR_1.2.1 magrittr_1.5 evaluate_0.8
#> [9] scales_0.3.0 stringi_1.0-1 rmarkdown_0.8.1 labeling_0.3
#> [13] tools_3.2.3 stringr_1.0.0 munsell_0.4.2 yaml_2.1.13
#> [17] colorspace_1.2-6 htmltools_0.2.6 knitr_1.12
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