DEVELOPMENT... OpenML
Run
157044

Run 157044

Task 3706 (Supervised Classification) stock Uploaded 11-02-2015 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)_I10
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
0.9846
Per class
F measure
0.9347
Per class
Kappa
0.8694
KB relative information score
728.754
Mean absolute error
0.1277
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
0.9349
Per class
Predictive accuracy
0.9347
Prior entropy
0.9995
Recall
0.9347
Per class
Relative absolute error
0.2556
Root mean prior squared error
0.4998
Root mean squared error
0.2271
Root relative squared error
0.4545
SciMark benchmark
929.566
UserCPU time millis
20
UserCPU time millis training
20