DEVELOPMENT... OpenML
Run
161021

Run 161021

Task 3706 (Supervised Classification) stock Uploaded 15-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I160
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.9933
Per class
F measure
0.9537
Per class
Kappa
0.9073
KB relative information score
854.2239
Mean absolute error
0.0521
Mean prior absolute error
0.4996
Number of instances
950
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9538
Per class
Predictive accuracy
0.9537
Prior entropy
0.9995
Recall
0.9537
Per class
Relative absolute error
0.1042
Root mean prior squared error
0.4998
Root mean squared error
0.1799
Root relative squared error
0.3599
SciMark benchmark
938.4285
UserCPU time millis
290
UserCPU time millis training
290