DEVELOPMENT... OpenML
Run
161113

Run 161113

Task 3798 (Supervised Classification) fri_c1_250_10 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
0.9586
Per class
F measure
0.8917
Per class
Kappa
0.7798
KB relative information score
189.2206
Mean absolute error
0.1224
Mean prior absolute error
0.4929
Number of instances
250
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8921
Per class
Predictive accuracy
0.892
Prior entropy
0.9898
Recall
0.892
Per class
Relative absolute error
0.2483
Root mean prior squared error
0.4964
Root mean squared error
0.2906
Root relative squared error
0.5855
SciMark benchmark
936.6206
UserCPU time millis
120
UserCPU time millis training
120