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imlplots: interpretable machine learning plots

imlplots is an R package that provides an interactive Shiny based dashboard for visualizing feature effects in supervised learning models. The following plots are supported:

  • Partial Dependence Plots (PDP)
  • Individual Conditional Expectation (ICE) plots
  • Accumulated Local Effect (ALE) plots

A video showcases the functionalities of the imlplots package:
https://www.youtube.com/watch?v=V6NIHtrKKEA

Installation

# install.packages("devtools")
devtools::install_github('compstat-lmu/imlplots')
library(imlplots)

Quickstart

For a quickstart we take the popular Boston Housing dataset. We want to predict the median housing price in Boston.

print(summarizeColumns(boston)[, -c(5, 6, 7)], digits = 4)
##       name    type na     mean       min     max nlevs
## 1     crim numeric  0   3.6135   0.00632  88.976     0
## 2       zn numeric  0  11.3636   0.00000 100.000     0
## 3    indus numeric  0  11.1368   0.46000  27.740     0
## 4     chas  factor  0       NA  35.00000 471.000     2
## 5      nox numeric  0   0.5547   0.38500   0.871     0
## 6       rm numeric  0   6.2846   3.56100   8.780     0
## 7      age numeric  0  68.5749   2.90000 100.000     0
## 8      dis numeric  0   3.7950   1.12960  12.127     0
## 9      rad  factor  0       NA  17.00000 132.000     9
## 10     tax numeric  0 408.2372 187.00000 711.000     0
## 11 ptratio numeric  0  18.4555  12.60000  22.000     0
## 12   black numeric  0 356.6740   0.32000 396.900     0
## 13   lstat numeric  0  12.6531   1.73000  37.970     0
## 14    medv numeric  0  22.5328   5.00000  50.000     0

There is a single function imlplots() to access the user interface. You need to specify three input arguments:

  • data - the input data frame
  • task- a regression or classification task, created with MLR
  • models - one or several models, trained with MLR

We create a regression task with medv as the target variable.

boston.task = makeRegrTask(data = boston, target = "medv")

You can train multiple models and compare them visually. We fit two different models: a random forest and a generalized linear model.

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

The trained model objects are saved in a list and passed to the imlplots() function:

mod.list = list(rf.mod, glm.mod)

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

Code for Copy & Paste

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

References