Run
1003

Run 1003

Task 131 (Supervised Data Stream Classification) BNG(bridges_version2,nominal,1000000) Uploaded 08-04-2014 by Jan van Rijn
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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
0.9135
Per class
Confusion matrix
F measure
0.7089
Per class
Kappa
0.6197
KB relative information score
666672.2416
Mean absolute error
0.1138
Mean prior absolute error
0.2778
Number of instances
1000000
Per class
Precision
0.7043
Per class
Predictive accuracy
0.7205
Prior entropy
2.585
Ram hours
0.0002
Recall
0.7205
Per class
Relative absolute error
0.4097
Root mean prior squared error
0.3727
Root mean squared error
0.2577
Root relative squared error
0.6916
Run CPU time
38.16