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README.Rmd
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# Variable importance via oscillations <img src="man/figures/logo.png" align="right" width="150"/>
[](https://cran.r-project.org/package=vivo)
<img src="http://cranlogs.r-pkg.org/badges/grand-total/vivo" /> [](https://travis-ci.org/ModelOriented/vivo)
[](https://codecov.io/github/ModelOriented/vivo?branch=master)
[](http://drwhy.ai/#eXtraAI)
## Overview
The `vivo` package helps to calculate
**- instance level variable importance (local sensitivity).**
The measure is based on Ceteris Paribus profiles and can be calculated in eight variants. Select the variant
that suits your needs by setting parameters: `absolute_deviation`,
`point` and `density`.
**- model level variable importance (global sensitivity).**
The measure is based on Partial Dependence Profiles.
## vivo package
The main functions are `global_variable_importance()` and `local_variable_importance()`.
<img src="man/figures/vivo.png" align="center" width="600"/>
`vivo` is a part of [DrWhy](https://github.com/ModelOriented/DrWhy)
collection of tools for Visual Exploration, Explanation and Debugging of
Predictive Models.
## Installation
From CRAN
```{r}
install.packages("vivo")
```
From GitHub
```{r}
install.packages("devtools")
devtools::install_github("ModelOriented/vivo")
```
## Intuition
#### Local variable importance
Ceteris Paribus is a latin phrase meaning „other things held constant”
or „all else unchanged”. Ceteris Paribus Plots show how the model
response depends on changes in a single input variable, keeping all
other variables unchanged. They work for any Machine Learning model and
allow for model comparisons to better understand how a black model
works.
The measure is based on Ceteris Paribus profiles oscillations. In
particular, the larger influence of an explanatory variable on
prediction at a particular instance, the larger the deviation along the
corresponding Ceteris Paribus profile. For a variable that exercises
little or no influence on model prediction, the profile will be flat or
will barely change.
#### Global variable importance
Here we have a similar intuition as above, but we are looking at Partial Dependence Profiles, because they show how the prediction changes for the model, not only for observation.
## How to use
- [Example of local variable importance](https://modeloriented.github.io/vivo/articles/vignette_apartments_local.html)
- [Example of global variable importance](https://modeloriented.github.io/vivo/articles/vignette_apartments_global.html)
## References
- Ceteris Paribus profiles
- `ceteris_paribus()` from [`ingredients`](https://modeloriented.github.io/ingredients/reference/ceteris_paribus.html)
- [Introduction to Ceteris Paribus](https://pbiecek.github.io/ema/ceterisParibus.html)
- Partial Dependence Profiles
- `partial_dependence()` from [`ingredients`](https://modeloriented.github.io/ingredients/reference/partial_dependence.html)
- [Introduction to Partial Dependence Profiles](https://pbiecek.github.io/ema/partialDependenceProfiles.html)
The package was created as a part of master’s diploma thesis at Warsaw
University of Technology at Faculty of Mathematics and Information
Science by [Anna Kozak](https://github.com/kozaka93/).