Naive Bayes Classification Learner
Source:R/LearnerClassifNaiveBayes.R
mlr_learners_classif.naive_bayes.RdNaive 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():
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.
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)) {
# 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