DEVELOPMENT... OpenML
Run
160917

Run 160917

Task 3602 (Supervised Classification) visualizing_environmental 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
0.7134
Per class
F measure
0.6843
Per class
Kappa
0.3717
KB relative information score
37.3145
Mean absolute error
0.3366
Mean prior absolute error
0.499
Number of instances
111
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6894
Per class
Predictive accuracy
0.6847
Prior entropy
0.9986
Recall
0.6847
Per class
Relative absolute error
0.6746
Root mean prior squared error
0.4995
Root mean squared error
0.5074
Root relative squared error
1.0158
SciMark benchmark
936.6206
UserCPU time millis
20
UserCPU time millis training
20