# Featureless Classification Learner

Source:`R/LearnerClassifFeatureless.R`

`mlr_learners_classif.featureless.Rd`

A simple LearnerClassif which only analyzes the labels during train, ignoring all features.
Hyperparameter `method`

determines the mode of operation during prediction:

- mode:
Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction. Probabilities correspond to the relative frequency of the class labels in the training set.

- sample:
Randomly predict a label uniformly. Probabilities correspond to a uniform distribution of class labels, i.e. 1 divided by the number of classes.

- weighted.sample:
Randomly predict a label, with probability estimated from the training distribution. For consistency, probabilities are 1 for the sampled label and 0 for all other labels.

## Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function `lrn()`

:

```
$get("classif.featureless")
mlr_learnerslrn("classif.featureless")
```

## Meta Information

Task type: “classif”

Predict Types: “response”, “prob”

Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”

Required Packages: mlr3

## See also

Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

Package mlr3learners for a solid collection of essential learners.

Package mlr3extralearners for more learners.

`as.data.table(mlr_learners)`

for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.

Package mlr3viz for some generic visualizations.

Extension packages for additional task types:

mlr3proba for probabilistic supervised regression and survival analysis.

mlr3cluster for unsupervised clustering.

mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner:
`LearnerClassif`

,
`LearnerRegr`

,
`Learner`

,
`mlr_learners_classif.debug`

,
`mlr_learners_classif.rpart`

,
`mlr_learners_regr.debug`

,
`mlr_learners_regr.featureless`

,
`mlr_learners_regr.rpart`

,
`mlr_learners`

## Super classes

`mlr3::Learner`

-> `mlr3::LearnerClassif`

-> `LearnerClassifFeatureless`