DEVELOPMENT... OpenML
Run
2917

Run 2917

Task 2149 (Supervised Data Stream Classification) BNG(bridges_version1) Uploaded 29-04-2014 by Jason
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Flow

moa.HoeffdingTree(1)A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).
moa.HoeffdingTree(1)_bfalse
moa.HoeffdingTree(1)_c1.0E-7
moa.HoeffdingTree(1)_dNominalAttributeClassObserver
moa.HoeffdingTree(1)_e1000000
moa.HoeffdingTree(1)_g200
moa.HoeffdingTree(1)_lNBAdaptive
moa.HoeffdingTree(1)_m33554432
moa.HoeffdingTree(1)_nGaussianNumericAttributeClassObserver
moa.HoeffdingTree(1)_pfalse
moa.HoeffdingTree(1)_q0
moa.HoeffdingTree(1)_rfalse
moa.HoeffdingTree(1)_sInfoGainSplitCriterion
moa.HoeffdingTree(1)_t0.05
moa.HoeffdingTree(1)_zfalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

20 Evaluation measures

Area under ROC curve
0.8325
Per class
Confusion matrix
F measure
0.6306
Per class
Kappa
0.5159
KB relative information score
590219.6308
Mean absolute error
0.1344
Mean prior absolute error
0.2778
Number of instances
1000000
Per class
Precision
0.626
Per class
Predictive accuracy
0.6498
Prior entropy
2.585
Ram hours
0.0008
Recall
0.6498
Per class
Relative absolute error
0.4839
Root mean prior squared error
0.3727
Root mean squared error
0.3006
Root relative squared error
0.8066
Run CPU time
64.7