# MetricsWeighted v0.3.0

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## Weighted Metrics, Scoring Functions and Performance Measures for Machine Learning

Provides weighted versions of several metrics,
scoring functions and performance measures used in machine learning,
including average unit deviances of the Bernoulli, Tweedie, Poisson,
and Gamma distributions, see Jorgensen B. (1997, ISBN:
978-0412997112). The package also contains a weighted version of
generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN:
978-0805822236). Furthermore, 'dplyr' chains are supported.

## Readme

# MetricsWeighted

The goal of this package is to provide weighted versions of metrics, scoring functions and performance measures for machine learning.

## Installation

You can install the released version of MetricsWeighted from CRAN with:

```
install.packages("MetricsWeighted")
```

To get the bleeding edge version, you can run

```
library(devtools)
install_github("mayer79/MetricsWeighted")
```

## Application

There are two ways to apply the package. We will go through them in the following examples. Please have a look at the vignette on CRAN for further information and examples.

## Example 1: Directly apply the metrics

```
library(MetricsWeighted)
y <- 1:10
pred <- c(2:10, 14)
rmse(y, pred)
rmse(y, pred, w = 1:10)
r_squared(y, pred)
r_squared(y, pred, deviance_function = deviance_gamma)
```

## Example 2: Call the metrics through a common function that can be used within a `dplyr`

chain

```
dat <- data.frame(y = y, pred = pred)
performance(dat, actual = "y", predicted = "pred")
performance(dat, actual = "y", predicted = "pred", metrics = r_squared)
performance(dat, actual = "y", predicted = "pred",
metrics = list(rmse = rmse, `R-squared` = r_squared))
```

## Functions in MetricsWeighted

Name | Description | |

precision | Precision | |

recall | Recall | |

rmse | Root-Mean-Squared Error | |

deviance_normal | Normal Deviance | |

r_squared | Pseudo R-Squared | |

weighted_median | Weighted Median | |

weighted_mean | Weighted Mean | |

mape | Mean Absolute Percentage Error | |

medae | Median Absolute Error | |

performance | Performance | |

weighted_var | Weighted Variance | |

mse | Mean-Squared Error | |

weighted_quantile | Weighted Quantiles | |

logLoss | Log Loss/Binary Cross Entropy | |

mae | Mean Absolute Error | |

deviance_poisson | Poisson Deviance | |

AUC | Area under the ROC | |

classification_error | Classification Error | |

deviance_gamma | Gamma Deviance | |

deviance_tweedie | Tweedie Deviance | |

deviance_bernoulli | Bernoulli Deviance | |

accuracy | Accuracy | |

f1_score | F1 Score | |

gini_coefficient | Gini Coefficient | |

No Results! |

## Vignettes of MetricsWeighted

Name | ||

MetricsWeighted.Rmd | ||

No Results! |

## Last month downloads

## Details

Type | Package |

Date | 2019-11-08 |

License | GPL (>= 2) |

VignetteBuilder | knitr |

Encoding | UTF-8 |

LazyData | true |

RoxygenNote | 6.1.1 |

NeedsCompilation | no |

Packaged | 2019-11-08 15:00:11 UTC; Michael |

Repository | CRAN |

Date/Publication | 2019-11-08 18:10:02 UTC |

suggests | dplyr , knitr |

depends | R (>= 3.5.0) |

imports | stats |

Contributors | Christian Lorentzen |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/MetricsWeighted)](http://www.rdocumentation.org/packages/MetricsWeighted)
```