DEVELOPMENT... OpenML
Run
5348

Run 5348

Task 2188 (Supervised Data Stream Classification) mfeat-pixel Uploaded 01-05-2014 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
0.986
Per class
Confusion matrix
F measure
0.9485
Per class
Kappa
0.9428
KB relative information score
1892.2834
Mean absolute error
0.0105
Mean prior absolute error
0.18
Number of instances
2000
Per class
Precision
0.951
Per class
Predictive accuracy
0.9485
Prior entropy
3.3219
Ram hours
0
Recall
0.9485
Per class
Relative absolute error
0.0582
Root mean prior squared error
0.3
Root mean squared error
0.0999
Root relative squared error
0.3329
Run CPU time
0.3463