fmeffects: An R Package for Forward Marginal Effects 
This package implements forward marginal effects (FMEs), a model-agnostic framework for interpreting feature effects in machine learning models. FMEs are the simplest and most intuitive way to interpret feature effects - we explain here how they are computed and why they should be preferred to existing methods. Currently, fmeffects supports regression and (binary) classification models from the mlr3, tidymodels and caret libraries.
See here for an in-depth tutorial. The big advantage of FMEs is that they are interpreted similar to beta coefficients in linear regression models. Consider the following example: how does an increase in temperature (temp) by 1°C affect bike rentals (count)?
# Train a random forest on "bike" data
set.seed(123)
library(fmeffects)
library(mlr3verse)
data(bikes)
task = as_task_regr(x = bikes, id = "bikes", target = "count")
forest = lrn("regr.ranger")$train(task)# Compute effects for a trained model 'forest':
effects = fme(model = forest,
data = bikes,
target = "count",
feature = "temp",
step.size = 1)
plot(effects, jitter = c(0.2, 0))
On average, an increase in temperature by 1°C results in an increase in the predicted number of bike rentals by more than 2. This is called the average marginal effect (AME).
Let’s compute the AME for every feature of the model:
# Compute AMEs with default step sizes:
overview = ame(model = forest,
data = bikes,
target = "count")
summary(overview)#>
#> Model Summary Using Average Marginal Effects:
#>
#> Feature step.size AME SD 0.25 0.75 n
#> 1 season spring -29.472 31.5101 -39.955 -5.5139 548
#> 2 season summer 0.4772 22.5212 -9.0235 11.6321 543
#> 3 season fall 11.7452 28.5851 -2.4282 34.1763 539
#> 4 season winter 15.5793 24.6394 1.6525 26.2254 551
#> 5 year 0 -99.038 67.1788 -157.0608 -20.0628 364
#> 6 year 1 97.0566 60.521 21.9401 148.0847 363
#> 7 month 1 4.0814 13.3513 -1.2566 7.459 727
#> 8 holiday False -1.2178 21.6103 -9.1095 9.8232 21
#> 9 holiday True -13.738 25.3496 -32.6323 6.2019 706
#> 10 weekday Sat -55.0908 49.6534 -87.6489 -15.8843 622
#> 11 weekday Sun -85.1527 57.7791 -122.1504 -31.8105 622
#> 12 weekday Mon 10.7224 29.2179 -8.4101 30.4207 623
#> 13 weekday Tue 17.9396 25.728 1.1959 32.5073 625
#> 14 weekday Wed 20.4025 23.1599 1.3386 32.8358 623
#> 15 weekday Thu 19.4455 24.1105 -0.3097 33.4997 624
#> 16 weekday Fri 1.7712 35.3088 -24.8956 29.5147 623
#> 17 workingday False -204.1875 89.3882 -257.144 -142.4332 496
#> 18 workingday True 161.0619 62.5733 118.9398 209.6916 231
#> 19 weather clear 26.1983 41.7886 3.5991 25.9257 284
#> 20 weather misty 3.023 32.8661 -9.1498 0.973 513
#> 21 weather rain -55.3083 53.0127 -94.4096 -5.481 657
#> 22 temp 1 2.3426 7.1269 -0.4294 4.5534 727
#> 23 humidity 0.01 -0.2749 2.626 -0.3249 0.3504 727
#> 24 windspeed 1 0.0052 2.4318 -0.1823 0.2318 727