DEVELOPMENT... OpenML
Run
57021

Run 57021

Task 6703 (Supervised Data Stream Classification) BNG(solar-flare) Uploaded 12-11-2014 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.

18 Evaluation measures

Area under ROC curve
0.9085
Per class
Confusion matrix
F measure
0.6985
Per class
Kappa
0.6445
KB relative information score
445023.8933
Mean absolute error
0.1207
Mean prior absolute error
0.2778
Number of instances
663552
Per class
Precision
0.69
Per class
Predictive accuracy
0.727
Prior entropy
2.585
Recall
0.727
Per class
Relative absolute error
0.4346
Root mean prior squared error
0.3727
Root mean squared error
0.2464
Root relative squared error
0.6611