DEVELOPMENT... OpenML
Run
158129

Run 158129

Task 3767 (Supervised Classification) fri_c0_1000_50 Uploaded 12-02-2015 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)_I20
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
0.9413
Per class
F measure
0.858
Per class
Kappa
0.7159
KB relative information score
611.1106
Mean absolute error
0.2068
Mean prior absolute error
0.4998
Number of instances
1000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.858
Per class
Predictive accuracy
0.858
Prior entropy
0.9997
Recall
0.858
Per class
Relative absolute error
0.4137
Root mean prior squared error
0.4999
Root mean squared error
0.3128
Root relative squared error
0.6258
SciMark benchmark
947.9494
UserCPU time millis
220
UserCPU time millis training
220