Classification Naive Bayes Learner From Weka

Naive Bayes Classifier Using Estimator Classes. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

• output_debug_info:

  • original id: output-debug-info

• do_not_check_capabilities:

  • original id: do-not-check-capabilities

• num_decimal_places:

  • original id: num-decimal-places

• batch_size:

  • original id: batch-size

• Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.naive_bayes_weka")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
K	logical	FALSE	TRUE, FALSE	-
D	logical	FALSE	TRUE, FALSE	-
O	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		\([1, \infty)\)
batch_size	integer	100		\([1, \infty)\)
options	untyped	NULL		-

References

John GH, Langley P (1995). “Estimating Continuous Distributions in Bayesian Classifiers.” In Eleventh Conference on Uncertainty in Artificial Intelligence, 338-345.

See also

• 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).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNaiveBayesWeka

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerClassifNaiveBayesWeka$new()

• LearnerClassifNaiveBayesWeka$marshal()

• LearnerClassifNaiveBayesWeka$unmarshal()

• LearnerClassifNaiveBayesWeka$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$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayesWeka$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifNaiveBayesWeka$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifNaiveBayesWeka$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayesWeka$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.naive_bayes_weka")
print(learner)
#> 
#> ── <LearnerClassifNaiveBayesWeka> (classif.naive_bayes_weka): Naive Bayes ──────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: marshal, missings, multiclass, and twoclass
#> • Other settings: use_weights = 'error'

# 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(learner$model)
#> Naive Bayes Classifier
#> 
#>                  Class
#> Attribute            M       R
#>                 (0.52)  (0.48)
#> ===============================
#> V1
#>   mean           0.0339  0.0227
#>   std. dev.      0.0232  0.0147
#>   weight sum         72      67
#>   precision      0.0011  0.0011
#> 
#> V10
#>   mean           0.2332  0.1677
#>   std. dev.      0.1112  0.1215
#>   weight sum         72      67
#>   precision      0.0044  0.0044
#> 
#> V11
#>   mean           0.2784  0.1797
#>   std. dev.       0.113  0.1187
#>   weight sum         72      67
#>   precision      0.0048  0.0048
#> 
#> V12
#>   mean           0.3045  0.1862
#>   std. dev.      0.1244  0.1302
#>   weight sum         72      67
#>   precision       0.005   0.005
#> 
#> V13
#>   mean            0.327  0.2237
#>   std. dev.      0.1268  0.1284
#>   weight sum         72      67
#>   precision      0.0052  0.0052
#> 
#> V14
#>   mean            0.342   0.272
#>   std. dev.      0.1652  0.1542
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V15
#>   mean           0.3465  0.3136
#>   std. dev.      0.1954  0.2144
#>   weight sum         72      67
#>   precision      0.0066  0.0066
#> 
#> V16
#>   mean           0.3929  0.3825
#>   std. dev.      0.2186  0.2537
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V17
#>   mean           0.4311   0.415
#>   std. dev.      0.2477  0.2937
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V18
#>   mean           0.4708  0.4402
#>   std. dev.      0.2507  0.2747
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V19
#>   mean           0.5519   0.466
#>   std. dev.      0.2502   0.265
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V2
#>   mean           0.0432  0.0327
#>   std. dev.      0.0304  0.0255
#>   weight sum         72      67
#>   precision      0.0013  0.0013
#> 
#> V20
#>   mean            0.621  0.5024
#>   std. dev.      0.2473  0.2613
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V21
#>   mean           0.6764  0.5492
#>   std. dev.      0.2397   0.248
#>   weight sum         72      67
#>   precision      0.0072  0.0072
#> 
#> V22
#>   mean           0.6986  0.5847
#>   std. dev.      0.2279  0.2676
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V23
#>   mean           0.7095  0.6222
#>   std. dev.      0.2248  0.2514
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V24
#>   mean           0.7208  0.6604
#>   std. dev.      0.2176  0.2326
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V25
#>   mean           0.7065  0.6747
#>   std. dev.      0.2315  0.2392
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V26
#>   mean           0.7221  0.6938
#>   std. dev.      0.2353  0.2353
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V27
#>   mean           0.7219  0.6844
#>   std. dev.      0.2642  0.2179
#>   weight sum         72      67
#>   precision      0.0076  0.0076
#> 
#> V28
#>   mean           0.6992  0.6719
#>   std. dev.      0.2688  0.1939
#>   weight sum         72      67
#>   precision      0.0074  0.0074
#> 
#> V29
#>   mean           0.6332  0.6317
#>   std. dev.      0.2494   0.238
#>   weight sum         72      67
#>   precision      0.0074  0.0074
#> 
#> V3
#>   mean           0.0493  0.0372
#>   std. dev.       0.039  0.0294
#>   weight sum         72      67
#>   precision      0.0015  0.0015
#> 
#> V30
#>   mean           0.5521  0.5823
#>   std. dev.      0.1969  0.2373
#>   weight sum         72      67
#>   precision       0.007   0.007
#> 
#> V31
#>   mean           0.4509   0.532
#>   std. dev.      0.2064  0.1937
#>   weight sum         72      67
#>   precision      0.0066  0.0066
#> 
#> V32
#>   mean           0.4058  0.4475
#>   std. dev.      0.2003  0.2022
#>   weight sum         72      67
#>   precision      0.0061  0.0061
#> 
#> V33
#>   mean           0.3782  0.4272
#>   std. dev.      0.1898  0.1998
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V34
#>   mean           0.3363  0.4329
#>   std. dev.      0.1917  0.2425
#>   weight sum         72      67
#>   precision      0.0068  0.0068
#> 
#> V35
#>   mean           0.2878  0.4477
#>   std. dev.      0.2092  0.2654
#>   weight sum         72      67
#>   precision      0.0071  0.0071
#> 
#> V36
#>   mean           0.2767  0.4574
#>   std. dev.      0.2162   0.267
#>   weight sum         72      67
#>   precision      0.0073  0.0073
#> 
#> V37
#>   mean           0.2732  0.4103
#>   std. dev.      0.1977  0.2457
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V38
#>   mean           0.2924  0.3303
#>   std. dev.      0.1713  0.2105
#>   weight sum         72      67
#>   precision      0.0066  0.0066
#> 
#> V39
#>   mean           0.3049  0.3075
#>   std. dev.      0.1603  0.1976
#>   weight sum         72      67
#>   precision      0.0069  0.0069
#> 
#> V4
#>   mean           0.0628  0.0422
#>   std. dev.      0.0427   0.032
#>   weight sum         72      67
#>   precision      0.0021  0.0021
#> 
#> V40
#>   mean           0.2825  0.3177
#>   std. dev.      0.1564   0.188
#>   weight sum         72      67
#>   precision      0.0067  0.0067
#> 
#> V41
#>   mean           0.2767  0.2862
#>   std. dev.      0.1616  0.1748
#>   weight sum         72      67
#>   precision      0.0063  0.0063
#> 
#> V42
#>   mean           0.2899  0.2528
#>   std. dev.      0.1648  0.1661
#>   weight sum         72      67
#>   precision      0.0057  0.0057
#> 
#> V43
#>   mean           0.2658  0.2136
#>   std. dev.      0.1422  0.1375
#>   weight sum         72      67
#>   precision      0.0056  0.0056
#> 
#> V44
#>   mean           0.2264  0.1744
#>   std. dev.      0.1403  0.1125
#>   weight sum         72      67
#>   precision      0.0058  0.0058
#> 
#> V45
#>   mean           0.2144  0.1426
#>   std. dev.      0.1533  0.0985
#>   weight sum         72      67
#>   precision      0.0044  0.0044
#> 
#> V46
#>   mean           0.1755  0.1174
#>   std. dev.      0.1374  0.0974
#>   weight sum         72      67
#>   precision      0.0054  0.0054
#> 
#> V47
#>   mean             0.14     0.1
#>   std. dev.      0.0939  0.0701
#>   weight sum         72      67
#>   precision      0.0041  0.0041
#> 
#> V48
#>   mean           0.1063  0.0711
#>   std. dev.      0.0692  0.0516
#>   weight sum         72      67
#>   precision      0.0025  0.0025
#> 
#> V49
#>   mean           0.0625  0.0395
#>   std. dev.      0.0372  0.0322
#>   weight sum         72      67
#>   precision      0.0015  0.0015
#> 
#> V5
#>   mean           0.0867  0.0671
#>   std. dev.      0.0551  0.0483
#>   weight sum         72      67
#>   precision      0.0025  0.0025
#> 
#> V50
#>   mean           0.0218  0.0185
#>   std. dev.      0.0148  0.0128
#>   weight sum         72      67
#>   precision      0.0008  0.0008
#> 
#> V51
#>   mean           0.0204   0.012
#>   std. dev.      0.0149  0.0084
#>   weight sum         72      67
#>   precision      0.0009  0.0009
#> 
#> V52
#>   mean           0.0149  0.0106
#>   std. dev.      0.0108  0.0073
#>   weight sum         72      67
#>   precision      0.0007  0.0007
#> 
#> V53
#>   mean           0.0112  0.0105
#>   std. dev.      0.0073  0.0065
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V54
#>   mean           0.0121  0.0099
#>   std. dev.      0.0088  0.0052
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V55
#>   mean           0.0093  0.0088
#>   std. dev.       0.008   0.005
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V56
#>   mean           0.0089  0.0077
#>   std. dev.      0.0065  0.0048
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V57
#>   mean           0.0084  0.0081
#>   std. dev.      0.0066  0.0059
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V58
#>   mean           0.0092  0.0068
#>   std. dev.      0.0078  0.0051
#>   weight sum         72      67
#>   precision      0.0004  0.0004
#> 
#> V59
#>   mean           0.0089  0.0078
#>   std. dev.      0.0069  0.0057
#>   weight sum         72      67
#>   precision      0.0003  0.0003
#> 
#> V6
#>   mean           0.1122  0.0959
#>   std. dev.       0.053  0.0643
#>   weight sum         72      67
#>   precision      0.0028  0.0028
#> 
#> V60
#>   mean           0.0065  0.0063
#>   std. dev.      0.0059  0.0039
#>   weight sum         72      67
#>   precision      0.0005  0.0005
#> 
#> V7
#>   mean           0.1283   0.112
#>   std. dev.      0.0564  0.0682
#>   weight sum         72      67
#>   precision      0.0027  0.0027
#> 
#> V8
#>   mean           0.1474   0.119
#>   std. dev.      0.0782  0.0842
#>   weight sum         72      67
#>   precision      0.0033  0.0033
#> 
#> V9
#>   mean           0.1966  0.1385
#>   std. dev.      0.1038  0.1039
#>   weight sum         72      67
#>   precision      0.0047  0.0047
#> 
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.1884058