Naive Bayes Classification Learner

Naive Bayes classification. Calls e1071::naiveBayes() from package e1071.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.naive_bayes")
lrn("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: mlr3::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

Methods

Public methods

• LearnerClassifNaiveBayes$new()

• LearnerClassifNaiveBayes$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayes$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("e1071", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.naive_bayes")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}
#> 
#> ── <LearnerClassifNaiveBayes> (classif.naive_bayes): Naive Bayes ───────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3learners, and e1071
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error'
#> 
#> Naive Bayes Classifier for Discrete Predictors
#> 
#> Call:
#> naiveBayes.default(x = x, y = y)
#> 
#> A-priori probabilities:
#> y
#>         M         R 
#> 0.5035971 0.4964029 
#> 
#> Conditional probabilities:
#>    V1
#> y         [,1]       [,2]
#>   M 0.03543571 0.02767424
#>   R 0.02228551 0.01563380
#> 
#>    V10
#> y        [,1]      [,2]
#>   M 0.2697457 0.1487176
#>   R 0.1545116 0.1139396
#> 
#>    V11
#> y        [,1]      [,2]
#>   M 0.3123100 0.1337594
#>   R 0.1678681 0.1172883
#> 
#>    V12
#> y        [,1]      [,2]
#>   M 0.3180171 0.1287576
#>   R 0.1843304 0.1330960
#> 
#>    V13
#> y        [,1]      [,2]
#>   M 0.3217900 0.1408331
#>   R 0.2235942 0.1381404
#> 
#>    V14
#> y        [,1]      [,2]
#>   M 0.3355800 0.1747611
#>   R 0.2614826 0.1632588
#> 
#>    V15
#> y        [,1]      [,2]
#>   M 0.3373357 0.2104526
#>   R 0.2936449 0.2136807
#> 
#>    V16
#> y        [,1]      [,2]
#>   M 0.3805800 0.2302481
#>   R 0.3624333 0.2429908
#> 
#>    V17
#> y        [,1]      [,2]
#>   M 0.4072243 0.2496591
#>   R 0.3864942 0.2844616
#> 
#>    V18
#> y        [,1]      [,2]
#>   M 0.4462057 0.2529618
#>   R 0.4162551 0.2636209
#> 
#>    V19
#> y        [,1]      [,2]
#>   M 0.5281171 0.2535792
#>   R 0.4405609 0.2541393
#> 
#>    V2
#> y         [,1]       [,2]
#>   M 0.04383286 0.03256179
#>   R 0.03071884 0.02591941
#> 
#>    V20
#> y        [,1]      [,2]
#>   M 0.6194171 0.2450133
#>   R 0.4682043 0.2555754
#> 
#>    V21
#> y        [,1]      [,2]
#>   M 0.6700914 0.2550814
#>   R 0.5022739 0.2500458
#> 
#>    V22
#> y        [,1]      [,2]
#>   M 0.6696386 0.2504505
#>   R 0.5301913 0.2718165
#> 
#>    V23
#> y        [,1]      [,2]
#>   M 0.6735243 0.2583763
#>   R 0.5767841 0.2549052
#> 
#>    V24
#> y        [,1]      [,2]
#>   M 0.6817771 0.2477394
#>   R 0.6165188 0.2435307
#> 
#>    V25
#> y        [,1]      [,2]
#>   M 0.6733243 0.2357445
#>   R 0.6279000 0.2642112
#> 
#>    V26
#> y        [,1]      [,2]
#>   M 0.7102514 0.2208602
#>   R 0.6635797 0.2480646
#> 
#>    V27
#> y        [,1]      [,2]
#>   M 0.7335686 0.2594018
#>   R 0.6751319 0.2278835
#> 
#>    V28
#> y        [,1]      [,2]
#>   M 0.7301329 0.2580287
#>   R 0.6794754 0.2051231
#> 
#>    V29
#> y        [,1]      [,2]
#>   M 0.6613400 0.2387862
#>   R 0.6400826 0.2445375
#> 
#>    V3
#> y         [,1]       [,2]
#>   M 0.04623857 0.03125455
#>   R 0.03513768 0.03054171
#> 
#>    V30
#> y        [,1]      [,2]
#>   M 0.5808114 0.2007254
#>   R 0.6011145 0.2277081
#> 
#>    V31
#> y        [,1]      [,2]
#>   M 0.4675314 0.2103041
#>   R 0.5464652 0.1955999
#> 
#>    V32
#> y        [,1]      [,2]
#>   M 0.4147329 0.2104932
#>   R 0.4547971 0.2094995
#> 
#>    V33
#> y        [,1]      [,2]
#>   M 0.3923986 0.1985727
#>   R 0.4519870 0.2119254
#> 
#>    V34
#> y        [,1]      [,2]
#>   M 0.3735514 0.2066956
#>   R 0.4465536 0.2490133
#> 
#>    V35
#> y        [,1]      [,2]
#>   M 0.3428857 0.2567624
#>   R 0.4571565 0.2585889
#> 
#>    V36
#> y        [,1]      [,2]
#>   M 0.3217357 0.2680625
#>   R 0.4732043 0.2611805
#> 
#>    V37
#> y        [,1]      [,2]
#>   M 0.3241429 0.2415785
#>   R 0.4349377 0.2495730
#> 
#>    V38
#> y        [,1]      [,2]
#>   M 0.3328471 0.2071929
#>   R 0.3744580 0.2306755
#> 
#>    V39
#> y        [,1]      [,2]
#>   M 0.3396243 0.1889006
#>   R 0.3400812 0.2285827
#> 
#>    V4
#> y         [,1]       [,2]
#>   M 0.05872714 0.03661336
#>   R 0.04082899 0.03138596
#> 
#>    V40
#> y        [,1]      [,2]
#>   M 0.3009229 0.1610126
#>   R 0.3448826 0.2076318
#> 
#>    V41
#> y        [,1]      [,2]
#>   M 0.2681014 0.1587514
#>   R 0.3103406 0.1880790
#> 
#>    V42
#> y        [,1]      [,2]
#>   M 0.2881229 0.1630567
#>   R 0.2667536 0.1775752
#> 
#>    V43
#> y        [,1]      [,2]
#>   M 0.2850800 0.1470607
#>   R 0.2160116 0.1388151
#> 
#>    V44
#> y        [,1]      [,2]
#>   M 0.2582271 0.1527208
#>   R 0.1732696 0.1121297
#> 
#>    V45
#> y        [,1]      [,2]
#>   M 0.2504386 0.1695908
#>   R 0.1435638 0.1047682
#> 
#>    V46
#> y        [,1]      [,2]
#>   M 0.2056514 0.1488980
#>   R 0.1205391 0.1009336
#> 
#>    V47
#> y         [,1]       [,2]
#>   M 0.15181714 0.09690115
#>   R 0.09809565 0.07429230
#> 
#>    V48
#> y         [,1]       [,2]
#>   M 0.11341000 0.06452643
#>   R 0.07255362 0.05198698
#> 
#>    V49
#> y         [,1]       [,2]
#>   M 0.06547571 0.03702589
#>   R 0.04014783 0.03362529
#> 
#>    V5
#> y         [,1]       [,2]
#>   M 0.07940714 0.04838741
#>   R 0.06065362 0.04717707
#> 
#>    V50
#> y         [,1]       [,2]
#>   M 0.02440714 0.01607869
#>   R 0.01810435 0.01346869
#> 
#>    V51
#> y         [,1]        [,2]
#>   M 0.01914286 0.015101767
#>   R 0.01269855 0.009320242
#> 
#>    V52
#> y         [,1]        [,2]
#>   M 0.01523571 0.009836107
#>   R 0.01104058 0.007716108
#> 
#>    V53
#> y         [,1]        [,2]
#>   M 0.01109429 0.007921533
#>   R 0.01021014 0.006667175
#> 
#>    V54
#> y          [,1]        [,2]
#>   M 0.012231429 0.008715912
#>   R 0.009402899 0.005615131
#> 
#>    V55
#> y          [,1]        [,2]
#>   M 0.010304286 0.009458796
#>   R 0.008531884 0.005390173
#> 
#>    V56
#> y          [,1]        [,2]
#>   M 0.008935714 0.006649377
#>   R 0.007288406 0.004663959
#> 
#>    V57
#> y          [,1]        [,2]
#>   M 0.007721429 0.006113679
#>   R 0.007795652 0.005700940
#> 
#>    V58
#> y          [,1]       [,2]
#>   M 0.009311429 0.00867927
#>   R 0.006965217 0.00515295
#> 
#>    V59
#> y          [,1]        [,2]
#>   M 0.007838571 0.006536881
#>   R 0.007949275 0.005600830
#> 
#>    V6
#> y         [,1]       [,2]
#>   M 0.10806714 0.04870222
#>   R 0.08961739 0.06079483
#> 
#>    V60
#> y          [,1]        [,2]
#>   M 0.006114286 0.003986691
#>   R 0.006372464 0.003845314
#> 
#>    V7
#> y        [,1]       [,2]
#>   M 0.1299900 0.05840901
#>   R 0.1117725 0.06876975
#> 
#>    V8
#> y        [,1]       [,2]
#>   M 0.1549586 0.08907895
#>   R 0.1172609 0.08343406
#> 
#>    V9
#> y        [,1]       [,2]
#>   M 0.2232171 0.11870299
#>   R 0.1290261 0.09934613
#> 
#> classif.ce 
#>  0.3043478