DEVELOPMENT... OpenML
Run
4274

Run 4274

Task 2197 (Supervised Data Stream Classification) credit-approval 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
0.8633
Per class
Confusion matrix
F measure
0.801
Per class
Kappa
0.5963
KB relative information score
398.5111
Mean absolute error
0.2154
Mean prior absolute error
0.5
Number of instances
690
Per class
Precision
0.801
Per class
Predictive accuracy
0.8014
Prior entropy
1
Recall
0.8014
Per class
Relative absolute error
0.4308
Root mean prior squared error
0.5
Root mean squared error
0.4114
Root relative squared error
0.8228
Run CPU time
0.05