README.Rmd

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# imlplots: interpretable machine learning plots
 `imlplots ` is an R package that provide an interactive Shiny dashboard for three kinds of Interpretable Machine Learning (IML) plots
 
* Partial Dependence Plots (PDP)
* Individual Conditional Expectation (ICE) plots
* Accumulated Local Effect (ALE) plots



```{r global_options, include=FALSE}
library(knitr)
opts_chunk$set(echo=TRUE, warning=FALSE, message=FALSE)
set.seed(42)
load("data/boston.rda")
library(mlr)
```

# Installation 
The package can be installed directly from github with devtools
```{r, results = 'hide', eval = FALSE}
# install.packages("devtools")
devtools::install_github('juliafried/imlplots')
library(imlplots)
```


# Quickstart
You can fit classification and regression problems from the `mlr` package and analyse possible interaction effects in a Shiny dasbhoard.

For quickstart we take the popular Boston Housing data, where we want to predict the median housing price in Boston.
```{r}
print(summarizeColumns(boston)[, -c(5, 6, 7)], digits = 4)
```

For using `imlplots` Shiny dashboard, three input arguments need to be specified

* `data` - the input data
* `task`- the learning task
* `models` - one or several trained models

We create a regression task with `medv` as target variable.
The task structure is determined by `mlr` package.
```{r}
boston.task = makeRegrTask(data = boston, target = "medv")
```

The `imlplots` dashboard allows the comparison of multiple learning algorithms, therefore we fit two different models - first a random forest and second a GLM.
```{r}
rf.mod = train("regr.randomForest", boston.task)
glm.mod = train("regr.glm", boston.task)
```

The input for the Shiny app is a list of learners.
```{r}
mod.list = list(rf.mod, glm.mod)
```

Now the Shiny app can be used.
```{r, eval=FALSE}
imlplots(data = boston, task = boston.task, models = mod.list)
```

## Code for Copy & Paste
```{r, eval=FALSE}
boston.task = makeRegrTask(data = boston, target = "medv")

rf.mod = train("regr.randomForest", boston.task)
glm.mod = train("regr.glm", boston.task)
mod.list = list(rf.mod, glm.mod)

imlplots(data = boston, task = boston.task, models = mod.list)
```

## Further Examples

* Check out our [Wiki](https://github.com/juliafried/imlplots/wiki)
* [Vignette](https://github.com/juliafried/imlplots/raw/master/vignettes/imlplots.pdf)

# References

* [References](https://github.com/juliafried/imlplots/raw/master/paper/references.pdf)