DEVELOPMENT... OpenML
Run
4090

Run 4090

Task 2228 (Supervised Data Stream Classification) zoo Uploaded 30-04-2014 by Felicia West
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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
0.9221
Per class
Confusion matrix
F measure
0.873
Per class
Kappa
0.8314
KB relative information score
86.5738
Mean absolute error
0.0391
Mean prior absolute error
0.2449
Number of instances
101
Per class
Precision
0.8795
Per class
Predictive accuracy
0.8713
Prior entropy
2.8074
Recall
0.8713
Per class
Relative absolute error
0.1597
Root mean prior squared error
0.3499
Root mean squared error
0.1857
Root relative squared error
0.5307