Measure

NaiveBayesErrRate

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Value per dataset

MercuryinBass (1) -1
mv (1) -1
kdd_ipums_la_98-small (1) 0.5434869739479
qqdefects_numeric (1) -1
gina_prior2 (1) 0.29008073817762
ar1 (1) 0.24793388429752
analcatdata_challenger (2) 0.08695652173913
datatrieve (1) 0.2
puma32H (1) -1
kc1-top5 (1) 0.10344827586207
sylva_prior (1) 0.024730809308788
nasa_numeric (1) -1
mw1 (1) 0.15632754342432
mc1 (1) 0.066025776463131
ar6 (1) 0.14851485148515
glass (2) 0.44392523364486
mfeat-pixel (2) 0.0425
soybean (2) 0.14494875549048
ar5 (1) 0.16666666666667
FacultySalaries (1) -1
Brainsize (1) -1
analcatdata_germangss (2) 0.1025
kdd_ipums_la_99-small (1) 0.5359565807327
dermatology (2) 0.0054644808743169
primary-tumor (2) 0.18289085545723
white-clover (1) 0.36507936507937
kdd_synthetic_control (1) 0
bridges (5) 0.16822429906542
vowel (3) 0.05959595959596
pc2 (1) 0.037037037037037
white-clover (2) 0.31746031746032
ar3 (1) 0.15873015873016
jEdit_4.0_4.2 (1) 0.3029197080292
cm1_req (1) 0.53932584269663
bridges (2) 0.37142857142857
postoperative-patient-data (1) 0.36666666666667
trains (1) 0.3
BNG(anneal,nominal,1000000) (1) 0.118746
BNG(anneal.ORIG,nominal,1000000) (1) 0.171217
BNG(kr-vs-kp) (1) 0.148651
BNG(letter,nominal,1000000) (1) 0.536816
BNG(autos,nominal,1000000) (1) 0.311771
BNG(lymph,nominal,1000000) (1) 0.123115
BNG(mfeat-fourier,nominal,1000000) (1) 0.14656
sonar (1) 0.32692307692308
cylinder-bands (1) 0.31111111111111
nursery (1) 0.097530864197531
BNG(labor,nominal,1000000) (1) 0.069135
BNG(breast-cancer,nominal,1000000) (1) 0.247271
ionosphere (1) 0.17378917378917
lymph (1) 0.15540540540541
vowel (1) 0.41717171717172
colic (1) 0.19565217391304
autoUniv-au1-1000 (1) 0.265
autoUniv-au4-2500 (1) 0.472
autoUniv-au6-750 (1) 0.812
autoUniv-au7-1100 (1) 0.66272727272727
autoUniv-au7-700 (1) 0.56714285714286
autoUniv-au7-500 (1) 0.614
autoUniv-au6-1000 (1) 0.827
abalone (3) 0.42422791477137
bank-marketing (2) 0.13249281132493