Package website: release | dev

{mlr3filters} adds feature selection filters to mlr3. The implemented filters can be used stand-alone, or as part of a machine learning pipeline in combination with mlr3pipelines and the filter operator.

Wrapper methods for feature selection are implemented in mlr3fselect. Learners which support the extraction feature importance scores can be combined with a filter from this package for embedded feature selection.

CRAN version

`install.packages("mlr3filters")`

Development version

`::install_github("mlr-org/mlr3filters") remotes`

```
set.seed(1)
library("mlr3")
library("mlr3filters")
= tsk("sonar")
task = flt("auc")
filter head(as.data.table(filter$calculate(task)))
```

```
## feature score
## 1: V11 0.2811368
## 2: V12 0.2429182
## 3: V10 0.2327018
## 4: V49 0.2312622
## 5: V9 0.2308442
## 6: V48 0.2062784
```

Name | label | Task Types | Feature Types | Package |
---|---|---|---|---|

anova | ANOVA F-Test | Classif | Integer, Numeric | stats |

auc | Area Under the ROC Curve Score | Classif | Integer, Numeric | mlr3measures |

carscore | Correlation-Adjusted coRrelation Score | Regr | Numeric | care |

carsurvscore | Correlation-Adjusted coRrelation Survival Score | Surv | Integer, Numeric | carSurv, mlr3proba |

cmim | Minimal Conditional Mutual Information Maximization | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

correlation | Correlation | Regr | Integer, Numeric | stats |

disr | Double Input Symmetrical Relevance | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

find_correlation | Correlation-based Score | Classif & Regr | Integer, Numeric | stats |

importance | Importance Score | Universal | Logical, Integer, Numeric, Character, Factor, Ordered, POSIXct | |

information_gain | Information Gain | Classif & Regr | Integer, Numeric, Factor, Ordered | FSelectorRcpp |

jmi | Joint Mutual Information | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

jmim | Minimal Joint Mutual Information Maximization | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

kruskal_test | Kruskal-Wallis Test | Classif | Integer, Numeric | stats |

mim | Mutual Information Maximization | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

mrmr | Minimum Redundancy Maximal Relevancy | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

njmim | Minimal Normalised Joint Mutual Information Maximization | Classif & Regr | Integer, Numeric, Factor, Ordered | praznik |

performance | Predictive Performance | Universal | Logical, Integer, Numeric, Character, Factor, Ordered, POSIXct | |

permutation | Permutation Score | Universal | Logical, Integer, Numeric, Character, Factor, Ordered, POSIXct | |

relief | RELIEF | Classif & Regr | Integer, Numeric, Factor, Ordered | FSelectorRcpp |

selected_features | Embedded Feature Selection | Universal | Logical, Integer, Numeric, Character, Factor, Ordered, POSIXct | |

variance | Variance | Universal | Integer, Numeric | stats |

The following learners allow the extraction of variable importance
and therefore are supported by `FilterImportance`

:

```
## [1] "classif.featureless" "classif.ranger" "classif.rpart"
## [4] "classif.xgboost" "regr.featureless" "regr.ranger"
## [7] "regr.rpart" "regr.xgboost"
```

If your learner is not listed here but capable of extracting variable importance from the fitted model, the reason is most likely that it is not yet integrated in the package mlr3learners or the extra learner organization. Please open an issue so we can add your package.

Some learners need to have their variable importance measure “activated” during learner creation. For example, to use the “impurity” measure of Random Forest via the {ranger} package:

```
= tsk("iris")
task = lrn("classif.ranger")
lrn $param_set$values = list(importance = "impurity")
lrn
= flt("importance", learner = lrn)
filter $calculate(task)
filterhead(as.data.table(filter), 3)
```

```
## feature score
## 1: Petal.Length 43.19847
## 2: Petal.Width 43.11627
## 3: Sepal.Length 10.62848
```

`FilterPerformance`

is a univariate filter method which
calls `resample()`

with every predictor variable in the
dataset and ranks the final outcome using the supplied measure. Any
learner can be passed to this filter with `classif.rpart`

being the default. Of course, also regression learners can be passed if
the task is of type “regr”.

In many cases filtering is only one step in the modeling pipeline. To
select features based on filter values, one can use `PipeOpFilter`

from mlr3pipelines.

```
library(mlr3pipelines)
= tsk("spam")
task
# the `filter.frac` should be tuned
= po("filter", filter = flt("auc"), filter.frac = 0.5) %>>%
graph po("learner", lrn("classif.rpart"))
= as_learner(graph)
learner = resample(task, learner, rsmp("holdout")) rr
```