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, e1071
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: multiclass, twoclass
#> 
#> 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 0.03576143 0.02684351
#>   R 0.02209565 0.01559364
#> 
#>    V10
#> y        [,1]      [,2]
#>   M 0.2726129 0.1421090
#>   R 0.1588696 0.1185101
#> 
#>    V11
#> y        [,1]      [,2]
#>   M 0.3043686 0.1361172
#>   R 0.1724275 0.1171354
#> 
#>    V12
#> y        [,1]      [,2]
#>   M 0.3100729 0.1301461
#>   R 0.1840812 0.1321283
#> 
#>    V13
#> y        [,1]      [,2]
#>   M 0.3244829 0.1396395
#>   R 0.2238464 0.1393880
#> 
#>    V14
#> y        [,1]      [,2]
#>   M 0.3332643 0.1766075
#>   R 0.2611797 0.1673167
#> 
#>    V15
#> y        [,1]      [,2]
#>   M 0.3422671 0.2084315
#>   R 0.2911377 0.2162335
#> 
#>    V16
#> y        [,1]      [,2]
#>   M 0.3821657 0.2314869
#>   R 0.3597014 0.2464136
#> 
#>    V17
#> y        [,1]      [,2]
#>   M 0.4003786 0.2602862
#>   R 0.3879000 0.2829729
#> 
#>    V18
#> y        [,1]      [,2]
#>   M 0.4338314 0.2669955
#>   R 0.4174826 0.2629652
#> 
#>    V19
#> y        [,1]      [,2]
#>   M 0.5156686 0.2657469
#>   R 0.4363855 0.2472369
#> 
#>    V2
#> y         [,1]       [,2]
#>   M 0.04520857 0.03243771
#>   R 0.03037391 0.02639112
#> 
#>    V20
#> y        [,1]      [,2]
#>   M 0.5974200 0.2725917
#>   R 0.4626855 0.2485295
#> 
#>    V21
#> y        [,1]      [,2]
#>   M 0.6437671 0.2733279
#>   R 0.4987290 0.2413938
#> 
#>    V22
#> y        [,1]      [,2]
#>   M 0.6464457 0.2514303
#>   R 0.5333333 0.2673713
#> 
#>    V23
#> y        [,1]      [,2]
#>   M 0.6484114 0.2654438
#>   R 0.5817986 0.2564723
#> 
#>    V24
#> y        [,1]      [,2]
#>   M 0.6641671 0.2584230
#>   R 0.6187058 0.2446702
#> 
#>    V25
#> y        [,1]      [,2]
#>   M 0.6623857 0.2485371
#>   R 0.6287870 0.2645679
#> 
#>    V26
#> y        [,1]      [,2]
#>   M 0.7076943 0.2330708
#>   R 0.6625348 0.2469941
#> 
#>    V27
#> y        [,1]      [,2]
#>   M 0.7195943 0.2597404
#>   R 0.6733986 0.2273145
#> 
#>    V28
#> y        [,1]      [,2]
#>   M 0.7141914 0.2524030
#>   R 0.6753275 0.2076381
#> 
#>    V29
#> y        [,1]      [,2]
#>   M 0.6535357 0.2350319
#>   R 0.6370029 0.2426187
#> 
#>    V3
#> y         [,1]       [,2]
#>   M 0.05095714 0.03566286
#>   R 0.03528261 0.03073139
#> 
#>    V30
#> y        [,1]      [,2]
#>   M 0.5830171 0.2029212
#>   R 0.6036870 0.2251306
#> 
#>    V31
#> y        [,1]      [,2]
#>   M 0.4908371 0.2245753
#>   R 0.5530855 0.1932639
#> 
#>    V32
#> y        [,1]      [,2]
#>   M 0.4405857 0.2229313
#>   R 0.4626159 0.2098553
#> 
#>    V33
#> y        [,1]      [,2]
#>   M 0.4132214 0.2045360
#>   R 0.4520246 0.2101155
#> 
#>    V34
#> y        [,1]      [,2]
#>   M 0.3652014 0.2111059
#>   R 0.4513464 0.2464201
#> 
#>    V35
#> y        [,1]      [,2]
#>   M 0.3269557 0.2453314
#>   R 0.4705870 0.2602240
#> 
#>    V36
#> y        [,1]      [,2]
#>   M 0.3052214 0.2436184
#>   R 0.4893899 0.2529661
#> 
#>    V37
#> y        [,1]      [,2]
#>   M 0.3047500 0.2272292
#>   R 0.4426101 0.2468271
#> 
#>    V38
#> y        [,1]      [,2]
#>   M 0.3258229 0.2036651
#>   R 0.3810652 0.2318662
#> 
#>    V39
#> y        [,1]      [,2]
#>   M 0.3363629 0.1857309
#>   R 0.3444217 0.2273772
#> 
#>    V4
#> y         [,1]       [,2]
#>   M 0.06339286 0.03778774
#>   R 0.04149855 0.03131444
#> 
#>    V40
#> y        [,1]      [,2]
#>   M 0.3010771 0.1652422
#>   R 0.3480333 0.2052403
#> 
#>    V41
#> y        [,1]      [,2]
#>   M 0.2797414 0.1670650
#>   R 0.3106986 0.1877347
#> 
#>    V42
#> y        [,1]      [,2]
#>   M 0.2920700 0.1697089
#>   R 0.2650203 0.1774334
#> 
#>    V43
#> y        [,1]      [,2]
#>   M 0.2768086 0.1482344
#>   R 0.2154884 0.1404886
#> 
#>    V44
#> y        [,1]      [,2]
#>   M 0.2538214 0.1531734
#>   R 0.1726913 0.1145143
#> 
#>    V45
#> y        [,1]      [,2]
#>   M 0.2477743 0.1839631
#>   R 0.1440551 0.1054603
#> 
#>    V46
#> y        [,1]      [,2]
#>   M 0.2031357 0.1604962
#>   R 0.1198101 0.1015279
#> 
#>    V47
#> y         [,1]       [,2]
#>   M 0.16128429 0.10599481
#>   R 0.09678696 0.07508253
#> 
#>    V48
#> y        [,1]       [,2]
#>   M 0.1258614 0.07319167
#>   R 0.0715913 0.05264197
#> 
#>    V49
#> y         [,1]       [,2]
#>   M 0.07143286 0.03871689
#>   R 0.03937246 0.03389372
#> 
#>    V5
#> y         [,1]       [,2]
#>   M 0.08451143 0.05083893
#>   R 0.06198551 0.04737666
#> 
#>    V50
#> y         [,1]       [,2]
#>   M 0.02507571 0.01465077
#>   R 0.01812464 0.01339883
#> 
#>    V51
#> y         [,1]       [,2]
#>   M 0.02130857 0.01533759
#>   R 0.01250000 0.00940061
#> 
#>    V52
#> y         [,1]        [,2]
#>   M 0.01616714 0.010879668
#>   R 0.01099565 0.007860576
#> 
#>    V53
#> y          [,1]        [,2]
#>   M 0.011864286 0.007562571
#>   R 0.009981159 0.006709306
#> 
#>    V54
#> y          [,1]        [,2]
#>   M 0.013040000 0.008841542
#>   R 0.009675362 0.005728166
#> 
#>    V55
#> y          [,1]        [,2]
#>   M 0.010202857 0.009150750
#>   R 0.008584058 0.005399717
#> 
#>    V56
#> y          [,1]        [,2]
#>   M 0.009448571 0.006855395
#>   R 0.007128986 0.004714748
#> 
#>    V57
#> y          [,1]        [,2]
#>   M 0.008214286 0.006693048
#>   R 0.007617391 0.005784105
#> 
#>    V58
#> y          [,1]        [,2]
#>   M 0.010165714 0.008554055
#>   R 0.006963768 0.005151983
#> 
#>    V59
#> y          [,1]        [,2]
#>   M 0.009668571 0.007351891
#>   R 0.008030435 0.005618177
#> 
#>    V6
#> y         [,1]       [,2]
#>   M 0.11148571 0.05276747
#>   R 0.09168406 0.06398966
#> 
#>    V60
#> y          [,1]        [,2]
#>   M 0.006977143 0.006143930
#>   R 0.006447826 0.003817976
#> 
#>    V7
#> y        [,1]       [,2]
#>   M 0.1342243 0.06446209
#>   R 0.1105913 0.06909169
#> 
#>    V8
#> y        [,1]       [,2]
#>   M 0.1565043 0.09010940
#>   R 0.1163783 0.08433947
#> 
#>    V9
#> y        [,1]      [,2]
#>   M 0.2251914 0.1227192
#>   R 0.1316116 0.1031606
#> 
#> classif.ce 
#>  0.3913043