R: Kernel Density Estimator

mlr_learners_dens.kde {mlr3proba} | R Documentation

## Kernel Density Estimator

### Description

Calls kernels implemented in distr6 and the result is coerced to a distr6::Distribution.

### Details

The default bandwidth uses Silverman's rule-of-thumb for Gaussian kernels, however for non-Gaussian kernels it is recommended to use mlr3tuning to tune the bandwidth with cross-validation. Other density learners can be used for automated bandwidth selection. The default kernel is Epanechnikov (chosen to reduce dependencies).

### Dictionary

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

    LearnerDensKDE$new()
    mlr_learners$get("dens.kde")
    lrn("dens.kde")

### Meta Information

- Type: "dens"

- Predict Types: `pdf, distr`

- Feature Types: `integer, numeric`

- Properties: `missings`

- Packages: distr6

### Super classes

`mlr3::Learner` -> `mlr3proba::LearnerDens` -> `LearnerDensKDE`

### Methods

#### Public methods

- `LearnerDensKDE$new()`

- `LearnerDensKDE$clone()`

Inherited methods

- `mlr3::Learner$format()`

- `mlr3::Learner$help()`

- `mlr3::Learner$predict()`

- `mlr3::Learner$predict_newdata()`

- `mlr3::Learner$print()`

- `mlr3::Learner$reset()`

- `mlr3::Learner$train()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    LearnerDensKDE$new()

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    LearnerDensKDE$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### References

Silverman, W. B (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.

### See Also

Other density estimators: `mlr_learners_dens.hist`

[Package mlr3proba version 0.4.0 Index]