DEVELOPMENT... OpenML
Run
157056

Run 157056

Task 3718 (Supervised Classification) boston 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
0.918
Per class
F measure
0.8557
Per class
Kappa
0.7023
KB relative information score
316.4203
Mean absolute error
0.1903
Mean prior absolute error
0.4849
Number of instances
506
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8556
Per class
Predictive accuracy
0.8557
Prior entropy
0.9782
Recall
0.8557
Per class
Relative absolute error
0.3923
Root mean prior squared error
0.4924
Root mean squared error
0.3261
Root relative squared error
0.6622
SciMark benchmark
931.2336
UserCPU time millis
10
UserCPU time millis training
10