DEVELOPMENT... OpenML
Run
177258

Run 177258

Task 7363 (Supervised Data Stream Classification) BNG(letter,10000,5) Uploaded 23-02-2015 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
0.893
Per class
Confusion matrix
F measure
0.4744
Per class
Kappa
0.4556
KB relative information score
518829.2109
Mean absolute error
0.047
Mean prior absolute error
0.074
Number of instances
1000000
Per class
Precision
0.4764
Per class
Predictive accuracy
0.4766
Prior entropy
4.7004
Recall
0.4766
Per class
Relative absolute error
0.6348
Root mean prior squared error
0.1923
Root mean squared error
0.1634
Root relative squared error
0.8496