Run
1146

Run 1146

Task 191 (Supervised Data Stream Classification) Hyperplane_10_1E-4 Uploaded 10-04-2014 by Jan van Rijn
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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
0.9317
Per class
Confusion matrix
F measure
0.8905
Per class
Kappa
0.781
KB relative information score
827480.0378
Mean absolute error
0.0791
Mean prior absolute error
0.32
Number of instances
1000000
Per class
Precision
0.8905
Per class
Predictive accuracy
0.8905
Prior entropy
2.3219
Ram hours
0
Recall
0.8905
Per class
Relative absolute error
0.2473
Root mean prior squared error
0.4
Root mean squared error
0.192
Root relative squared error
0.48
Run CPU time
14.65