library(knitr) library(pander) library(ggplot2) knitr::opts_chunk$set(comment="#>", results="markup", fig.show='hold', fig.align="center", fig.height=8, fig.width=8, warning=FALSE, cache=TRUE) panderOptions("knitr.auto.asis", FALSE) theme_set( theme_bw(base_size=14) + theme( # legend.position="bottom", legend.key=element_rect(colour=NA), plot.margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm") ) ) set.seed(123) PROJECT_DIR <- Sys.getenv('PROJECT_DIR')
classifiers
Datadata(classifiers, package="AnalysisToolkit") str(classifiers, list.len=2, vec.len=2, strict.width = "cut") cPs <- classifiers[["classifier_points"]] cCs <- classifiers[["classifier_curves"]]
glance()
Binary Classifier Performance metrics(lift_dl(rbind))(map(cPs, glance)) %>% tibble::rownames_to_column("grader") %>% arrange(desc(acc)) %>% knitr::kable(digits=2)
glance()
Continuous Classifier Performance metrics(lift_dl(rbind))(map(cCs, glance_ClassifierCurve)) %>% tibble::rownames_to_column("grader") %>% arrange(desc(auc)) %>% knitr::kable(digits=2)
# Make ggplot base and collect curves roc_lines <- cCs %>% map_dfr(roc, .id = "grader") %>% { ggplot(., aes(x=x, y=y, col=grader)) + geom_line() + gg_roc_theme } # collect points into a ggproto roc_pts <- cPs %>% map_dfr(roc, .id="grader") %>% geom_point(data=.) # Combine roc_lines+roc_pts
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.