DEVELOPMENT... OpenML
Run
160794

Run 160794

Task 3501 (Supervised Classification) white-clover Uploaded 14-02-2015 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
0.7169
Per class
F measure
0.6363
Per class
Kappa
0.3018
KB relative information score
20.6689
Mean absolute error
0.1704
Mean prior absolute error
0.2721
Number of instances
63
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6297
Per class
Predictive accuracy
0.6508
Prior entropy
1.4097
Recall
0.6508
Per class
Relative absolute error
0.6264
Root mean prior squared error
0.3647
Root mean squared error
0.4037
Root relative squared error
1.1069
SciMark benchmark
945.7621
UserCPU time millis
170
UserCPU time millis training
170