DEVELOPMENT... OpenML
Run
156939

Run 156939

Task 3602 (Supervised Classification) visualizing_environmental 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
0.7476
Per class
F measure
0.7279
Per class
Kappa
0.4636
KB relative information score
32.8337
Mean absolute error
0.3657
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
0.7435
Per class
Predictive accuracy
0.7297
Prior entropy
0.9986
Recall
0.7297
Per class
Relative absolute error
0.7328
Root mean prior squared error
0.4995
Root mean squared error
0.4544
Root relative squared error
0.9097
SciMark benchmark
929.566
UserCPU time millis
10
UserCPU time millis training
10