## Conditional Predictive
Impact

David S. Watson, Marvin N. Wright

### Introduction

The conditional predictive impact (CPI) is a measure of conditional
independence. It can be calculated using any supervised learning
algorithm, loss function, and knockoff sampler. We provide statistical
inference procedures for the CPI without parametric assumptions or
sparsity constraints. The method works with continuous and categorical
data.

### Installation

The package is not on CRAN yet. To install the development version
from GitHub using `devtools`

, run

`devtools::install_github("bips-hb/cpi")`

### Examples

Calculate CPI for random forest on iris data with 5-fold cross
validation:

```
library(mlr3)
library(mlr3learners)
library(cpi)
cpi(task = tsk("iris"),
learner = lrn("classif.ranger", predict_type = "prob"),
resampling = rsmp("cv", folds = 5),
measure = "classif.logloss", test = "t")
```

### References

- Watson D. S. & Wright, M. N. (2021). Testing conditional
independence in supervised learning algorithms.
*Machine
Learning*. DOI: 10.1007/s10994-021-06030-6.