This vignette is an introduction to performing density estimation in mlr3proba.
Density estimation is the learning task to find the unknown distribution from which an i.i.d. data set is generated. We interpret this broadly, with this distribution not necessarily being continuous (so may possess a mass not density). The conditional case, where a distribution is predicted conditional on covariates, is known as ‘probabilistic supervised regression’, and will be implemented in mlr3proba in the near-future. In mlr3proba, (unconditional) density estimation is viewed as an unsupervised task, whereas probabilistic supervised regression (or conditional density estimation) is a supervised task
Unconditional density estimation is an unsupervised method. Hence, TaskDens
is an unsupervised task which inherits directly from Task
unlike TaskClassif
and TaskRegr
. However, TaskDens
still has a target
and a $truth
field defined by:
target
- the variable for which to estimate densitytruth
- the target
. This is not the true density which is always unknown.library(mlr3proba); library(mlr3) task = TaskDens$new(id = "mpg", backend = datasets::mtcars, target = "mpg") task #> <TaskDens:mpg> (32 x 11) #> * Target: mpg #> * Properties: - #> * Features (10): #> - dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt task$truth()[1:10] #> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2
Density learners have train
and predict
methods, though being unsupervised, ‘prediction’ is actually ‘estimation’. In training, a distr6 object is created, see here for full tutorials on how to access the pdf
, cdf
, and other important fields and methods. The predict method is simply a wrapper around self$model$pdf
and if available self$model$cdf
, i.e. evaluates the pdf/cdf at given points. Note that in prediction the points to evaluate the pdf and cdf are determined by the target
column in the TaskDens
object used for testing.
# create task and learner task_faithful = TaskDens$new(id = "eruptions", backend = datasets::faithful, target = "eruptions") learner = lrn("dens.kde") # train/test split train_set = sample(task_faithful$nrow, 0.8 * task_faithful$nrow) test_set = setdiff(seq_len(task_faithful$nrow), train_set) # fitting KDE and model inspection learner$train(task_faithful, row_ids = train_set) learner$model #> Norm_KDE class(learner$model) #> [1] "Distribution" "R6" # make predictions for new data prediction = learner$predict(task_faithful, row_ids = test_set)
Every PredictionDens
object can estimate:
pdf
- probability density functionSome learners can estimate:
cdf
- cumulative distribution functionprediction #> <PredictionDens> for 55 observations: #> row_id truth pdf #> 3 3.333 0.1094527 #> 8 3.600 0.2057676 #> 11 1.833 0.3015347 #> --- #> 241 4.150 0.4651316 #> 242 2.350 0.2310265 #> 272 4.467 0.4800183 # `pdf` is evaluated using the `log-loss` prediction$score() #> dens.logloss #> 1.145351