DEVELOPMENT... OpenML
Run
4668

Run 4668

Task 2210 (Supervised Data Stream Classification) haberman 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
0.4885
Per class
Confusion matrix
F measure
0.624
Per class
Kappa
-0.0078
KB relative information score
89.2788
Mean absolute error
0.3709
Mean prior absolute error
0.5
Number of instances
306
Per class
Precision
0.5928
Per class
Predictive accuracy
0.7255
Prior entropy
1
Recall
0.7255
Per class
Relative absolute error
0.7418
Root mean prior squared error
0.5
Root mean squared error
0.4518
Root relative squared error
0.9036
Run CPU time
0.06