DEVELOPMENT... OpenML
Run
158150

Run 158150

Task 3786 (Supervised Classification) visualizing_soil 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
0.9998
Per class
F measure
0.9999
Per class
Kappa
0.9998
KB relative information score
8635.1964
Mean absolute error
0.0004
Mean prior absolute error
0.495
Number of instances
8641
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9999
Per class
Predictive accuracy
0.9999
Prior entropy
0.9928
Recall
0.9999
Per class
Relative absolute error
0.0008
Root mean prior squared error
0.4975
Root mean squared error
0.0108
Root relative squared error
0.0216
SciMark benchmark
947.9494
UserCPU time millis
190
UserCPU time millis testing
10
UserCPU time millis training
180