#' Measures feature importance for any model provided
#'
#' @param train [data.frame | Required] Training set on which the model was trained
#' @param trainedModel [mlr obj | Required] MLR trained moodel object
#' @param seed [integer | Optional] Random seed number for reproducable results. Default of 1991
#' @param sample [numeric | Optional] A number between 0 - 1 to sub-sample the training set for faster computational time. Default of 0.1
#' @return List object containing a data.frame and a plot object.
#' @export
#' @examples
#' mod <- mlr::train(makeLearner("classif.ranger", predict.type = "prob"), iris.task)
#' featureImportance(train = iris, mod)
#' @author
#' Xander Horn
featureImportance <- function(train, trainedModel, seed = 1234, sample = 0.1){
library(iml)
library(caret)
library(mlr)
if (missing(train)) {
stop("Provide training set")
}
if (missing(trainedModel)) {
stop("Provide trained mlr model obj")
}
set.seed(seed)
feats <- trainedModel$features
y <- trainedModel$task.desc$target
temp <- train[caret::createDataPartition(y = train[, y], p = sample, list = FALSE), ]
predObj <- Predictor$new(model = trainedModel, data = temp[,feats], y = temp[, y])
if(trainedModel$task.desc$type == "classif"){
imp <- iml::FeatureImp$new(predictor = predObj, loss = "ce", parallel = TRUE)
} else {
temp[,y] <- as.numeric(temp[,y])
imp <- iml::FeatureImp$new(predictor = predObj, loss = "mae", parallel = TRUE)
}
plot <- plot(imp) + theme_bw() + ggtitle("Feature importance plot")
table <- imp$results
return(list(table = table, plot = plot))
}
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