set.seed(5658)
## load libraries
library(caret)
library(lime)
## partition the data
intrain <- createDataPartition(y = iris$Species, p = 0.8, list = F)
## create train and test data
train_data <- iris[intrain, ]
test_data <- iris[-intrain, ]
## train Random Forest model on train_data
model <- train(x = train_data[, 1:4], y = train_data[, 5],
method = 'rf')
## create an explainer object using train_data
explainer <- lime(train_data, model)
## explain new observations in test data
explanation <- explain(test_data[, 1], explainer,
n_labels = 1, n_features = 4)
EXPLAIN