4833
1
moa.AccuracyWeightedEnsemble_HoeffdingTree
1
Moa__16.04_April_2016_1.0
Moa implementation of AccuracyWeightedEnsemble
2016-10-26T10:47:39
English
Moa__16.04_April_2016
c
option
500
chunkSize: The chunk size used for classifier creation and evaluation.
f
option
10
numFolds: Number of cross-validation folds for candidate classifier testing.
l
baselearner
trees.HoeffdingTree -l NB -e 1000 -g 100 -c 0.01
learner: Classifier to train.
n
option
15.0
memberCount: The maximum number of classifier in an ensemble.
r
option
30.0
storedCount: The maximum number of classifiers to store and choose from when creating an ensemble.
l
4811
1
moa.HoeffdingTree
7
Moa__16.04_April_2016_1.0
Moa implementation of HoeffdingTree
2016-10-05T19:35:32
English
Moa__16.04_April_2016
b
flag
false
binarySplits: Only allow binary splits.
c
option
1.0E-7
splitConfidence: The allowable error in split decision, values closer to 0 will take longer to decide.
d
baselearner
NominalAttributeClassObserver
nominalEstimator: Nominal estimator to use.
e
option
1000000
memoryEstimatePeriod: How many instances between memory consumption checks.
g
option
200
gracePeriod: The number of instances a leaf should observe between split attempts.
l
option
NBAdaptive
leafprediction: Leaf prediction to use.
m
option
33554432
maxByteSize: Maximum memory consumed by the tree.
n
baselearner
GaussianNumericAttributeClassObserver
numericEstimator: Numeric estimator to use.
p
flag
false
noPrePrune: Disable pre-pruning.
q
option
0
nbThreshold: The number of instances a leaf should observe before permitting Naive Bayes.
r
flag
false
removePoorAtts: Disable poor attributes.
s
baselearner
InfoGainSplitCriterion
splitCriterion: Split criterion to use.
t
option
0.05
tieThreshold: Threshold below which a split will be forced to break ties.
z
flag
false
stopMemManagement: Stop growing as soon as memory limit is hit.
Verified_Supervised_Data_Stream_Classification
Verified_Supervised_Data_Stream_Classification