In juliafried/imlplots: Interactive Plots for Interpretable Machine Learning

imlplots: interpretable machine learning plots

imlplots is an R package that provide an interactive Shiny dashboard for three kinds of Interpretable Machine Learning (IML) plots

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

library(knitr)
opts_chunk$set(echo=TRUE, warning=FALSE, message=FALSE)
set.seed(42)
load("data/boston.rda")
library(mlr)

Installation

The package can be installed directly from github with devtools

# install.packages("devtools")
devtools::install_github('juliafried/imlplots')
library(imlplots)

Quickstart

You can fit classification and regression problems from the mlr package and analyse possible interaction effects in a Shiny dasbhoard.

For quickstart we take the popular Boston Housing data, where we want to predict the median housing price in Boston.

print(summarizeColumns(boston)[, -c(5, 6, 7)], digits = 4)

For using imlplots Shiny dashboard, three input arguments need to be specified

data - the input data
task- the learning task
models - one or several trained models

We create a regression task with medv as target variable. The task structure is determined by mlr package.

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

The imlplots dashboard allows the comparison of multiple learning algorithms, therefore we fit two different models - first a random forest and second a GLM.

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

The input for the Shiny app is a list of learners.

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

Now the Shiny app can be used.

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)