The `iml`

package can now handle bigger datasets. Earlier problems with exploding memory have been fixed for `FeatureEffect`

, `FeatureImp`

and `Interaction`

. It’s also possible now to compute `FeatureImp`

and `Interaction`

in parallel. This document describes how.

First we load some data, fit a random forest and create a Predictor object.

```
set.seed(42)
library("iml")
library("randomForest")
#> randomForest 4.7-1
#> Type rfNews() to see new features/changes/bug fixes.
data("Boston", package = "MASS")
rf <- randomForest(medv ~ ., data = Boston, n.trees = 10)
X <- Boston[which(names(Boston) != "medv")]
predictor <- Predictor$new(rf, data = X, y = Boston$medv)
```

Parallelization is supported via the {future} package. All you need to do is to choose a parallel backend via `future::plan()`

.

```
library("future")
library("future.callr")
# Creates a PSOCK cluster with 2 cores
plan("callr", workers = 2)
```

Now we can easily compute feature importance in parallel. This means that the computation per feature is distributed among the 2 cores I specified earlier.

```
imp <- FeatureImp$new(predictor, loss = "mae")
library("ggplot2")
#>
#> Attaching package: 'ggplot2'
#> The following object is masked from 'package:randomForest':
#>
#> margin
plot(imp)
```

That wasn’t very impressive, let’s actually see how much speed up we get by parallelization.

```
bench::system_time({
plan(sequential)
FeatureImp$new(predictor, loss = "mae")
})
#> process real
#> 2.66s 2.66s
bench::system_time({
plan("callr", workers = 2)
FeatureImp$new(predictor, loss = "mae")
})
#> process real
#> 2.71s 6.42s
```

A little bit of improvement, but not too impressive. Parallelization is more useful in the case where the model uses a lot of features or where the feature importance computation is repeated more often to get more stable results.

```
bench::system_time({
plan(sequential)
FeatureImp$new(predictor, loss = "mae", n.repetitions = 20)
})
#> process real
#> 7.87s 7.88s
bench::system_time({
plan("callr", workers = 2)
FeatureImp$new(predictor, loss = "mae", n.repetitions = 20)
})
#> process real
#> 2.82s 9.21s
```

Here the parallel computation is twice as fast as the sequential computation of the feature importance.

The parallelization also speeds up the computation of the interaction statistics:

```
bench::system_time({
plan(sequential)
Interaction$new(predictor)
})
#> process real
#> 13.7s 13.7s
bench::system_time({
plan("callr", workers = 2)
Interaction$new(predictor)
})
#> process real
#> 2.71s 11.76s
```

Same for `FeatureEffects`

:

```
bench::system_time({
plan(sequential)
FeatureEffects$new(predictor)
})
#> process real
#> 937ms 938ms
bench::system_time({
plan("callr", workers = 2)
FeatureEffects$new(predictor)
})
#> process real
#> 10.7s 17s
```