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https://github.com/tidymodels/yardstick

Tidy methods for measuring model performance
https://github.com/tidymodels/yardstick

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Tidy methods for measuring model performance

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README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-"

)

```



```{r load, include = FALSE, message = FALSE, warning = FALSE}

library(yardstick)

library(dplyr)

options(width = 100, digits = 3)

```



# yardstick  



[![Coverage Status](https://img.shields.io/codecov/c/github/tidymodels/yardstick/main.svg)](https://app.codecov.io/github/tidymodels/yardstick?branch=main)

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/yardstick)](https://CRAN.R-project.org/package=yardstick)

[![Downloads](https://cranlogs.r-pkg.org/badges/yardstick)](https://CRAN.R-project.org/package=yardstick)

[![R-CMD-check](https://github.com/tidymodels/yardstick/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/yardstick/actions/workflows/R-CMD-check.yaml)



## Overview



`yardstick` is a package to estimate how well models are working using [tidy data](https://doi.org/10.18637/jss.v059.i10) principles. See the [package webpage](https://yardstick.tidymodels.org/) for more information.



## Installation



To install the package:



```{r install, eval = FALSE}

install.packages("yardstick")



# Development version:

# install.packages("pak")

pak::pak("tidymodels/yardstick")

```



## Two class metric



For example, suppose you create a classification model and predict on a new data set. You might have data that looks like this:



```{r class-data}

library(yardstick)

library(dplyr)



head(two_class_example)

```



You can use a `dplyr`-like syntax to compute common performance characteristics of the model and get them back in a data frame:



```{r class-metrics}

metrics(two_class_example, truth, predicted)



# or



two_class_example %>%

  roc_auc(truth, Class1)

```



## Multiclass metrics



All classification metrics have at least one multiclass extension, with many

of them having multiple ways to calculate multiclass metrics.



```{r}

data("hpc_cv")

hpc_cv <- as_tibble(hpc_cv)

hpc_cv

```



```{r}

# Macro averaged multiclass precision

precision(hpc_cv, obs, pred)



# Micro averaged multiclass precision

precision(hpc_cv, obs, pred, estimator = "micro")

```



## Calculating metrics on resamples



If you have multiple resamples of a model, you can use a metric on a grouped

data frame to calculate the metric across all resamples at once.



This calculates multiclass ROC AUC using the method described in Hand, Till (2001),

and does it across all 10 resamples at once.



```{r}

hpc_cv %>%

  group_by(Resample) %>%

  roc_auc(obs, VF:L)

```



## Autoplot methods for easy visualization



Curve based methods such as `roc_curve()`, `pr_curve()` and `gain_curve()` all

have `ggplot2::autoplot()` methods that allow for powerful and easy visualization.



```{r roc-curves}

#| fig-alt: "Faceted ROC curve. 1-specificity along the x-axis, sensitivity along the y-axis. Facets include the classes F, L, M, and VF. Each facet shows 10 lines colored to correspond to a resample. All the lines are quite overlapping. With VF having the tightest and highest values."

library(ggplot2)



hpc_cv %>%

  group_by(Resample) %>%

  roc_curve(obs, VF:L) %>%

  autoplot()

```



## Contributing



This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.



- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).



- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/yardstick/issues).



- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.



- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).