Naive Bayes Classification Learner
Source:R/LearnerClassifNaiveBayes.R
mlr_learners_classif.naive_bayes.Rd
Naive Bayes classification.
Calls e1071::naiveBayes()
from package e1071.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("classif.naive_bayes")
mlr_learnerslrn("classif.naive_bayes")
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3learners, e1071
Parameters
Id | Type | Default | Range |
eps | numeric | 0 | \((-\infty, \infty)\) |
laplace | numeric | 0 | \([0, \infty)\) |
threshold | numeric | 0.001 | \((-\infty, \infty)\) |
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3extralearners for more learners.
Dictionary of Learners: mlr_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.
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:
mlr_learners_classif.cv_glmnet
,
mlr_learners_classif.glmnet
,
mlr_learners_classif.kknn
,
mlr_learners_classif.lda
,
mlr_learners_classif.log_reg
,
mlr_learners_classif.multinom
,
mlr_learners_classif.nnet
,
mlr_learners_classif.qda
,
mlr_learners_classif.ranger
,
mlr_learners_classif.svm
,
mlr_learners_classif.xgboost
,
mlr_learners_regr.cv_glmnet
,
mlr_learners_regr.glmnet
,
mlr_learners_regr.kknn
,
mlr_learners_regr.km
,
mlr_learners_regr.lm
,
mlr_learners_regr.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifNaiveBayes
Examples
if (requireNamespace("e1071", quietly = TRUE)) {
learner = mlr3::lrn("classif.naive_bayes")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClassifNaiveBayes:classif.naive_bayes>: Naive Bayes
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, e1071
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: multiclass, twoclass
#> [1] "eps" "laplace" "threshold"