Machine Learning and lime Package

# install.packages('devtools')
devtools::install_github('thomasp85/lime')

Skipping install of 'lime' from a github remote, the SHA1 (fa6b8dae) has not changed since last install.
  Use `force = TRUE` to force installation

library(lime)

Attaching package: ‘lime’

The following object is masked from ‘package:dplyr’:

    explain

The purpose of lime is to explain the predictions of black box classifiers. What this means is that for any given prediction and any given classifier it is able to determine a small set of features in the original data that has driven the outcome of the prediction. To learn more about the methodology of lime read the paper and visit the repository of the original implementation.

The lime package for R does not aim to be a line-by-line port of its Python counterpart. Instead it takes the ideas laid out in the original code and implements them in an API that is idiomatic to R.

An example

Out of the box lime supports models created using the caret and mlr frameworks. Support for other models are easy to achieve by adding a predict_model and model_type method for the given model.

The following shows how a random forest model is trained on the iris data set and how lime is then used to explain a set of new observations:

library(caret)
library(lime)
# Split up the data set
iris_test <- iris[1:5, 1:4]
iris_train <- iris[-(1:5), 1:4]
iris_lab <- iris[[5]][-(1:5)]
# Create Random Forest model on iris data
model <- train(iris_train, iris_lab, method = 'rf')
# Create an explainer object
explainer <- lime(iris_train, model)
# Explain new observation
explanation <- explain(iris_test, explainer, n_labels = 1, n_features = 2)
# The output is provided in a consistent tabular format and includes the
# output from the model.
head(explanation)

model2<- randomForest(Species~ ., data=iris[1:120,], ntree=1000, keep.forest=FALSE,
                          importance=TRUE)
varImpPlot(model2)

(predict(model2, newdata=iris[121:150,]))

Error in predict.randomForest(model2, newdata = iris[121:150, ]) : 
  No forest component in the object