MLcomp version alpha Statistics: 2046 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 13785

Creator	yorinasub17
Program	HyperPipes_weka_nominal
Dataset	molecular-biology-splice
Task type	MulticlassClassification
Created	1y363d ago
Download	Login required!

Status: Done!

Total Time	21s
Max Memory Usage	420M
Domain	MulticlassClassification
Learn time
Train error	0.606
Predict train time
Test error	0.641
Predict test time

Log file

./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [8s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [8s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [3s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	0m22.764s
user	0m8.685s
sys	0m0.640s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) HyperPipes_weka_nominal: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/misc/HyperPipes.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance". Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    NOTE: WEKA's Classifiers read a data in the .arff format. For Multiclass datasets, the SVMlight format converted to .arff multiclass format so they can be read by WEKA programs
  • (dataset:Dataset) molecular-biology-splice: 3190 examples, 244 features
  • (stripper:Program[Strip]) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • errorRate: 0.640543364681296
    • numErrors: 613
    • numExamples: 957
    • success: true
    • time: 1
  • predict:
    • success: true
    • time: 3
  • strip:
    • success: true
    • time: 1
• doTrain:
  • evaluate:
    • errorRate: 0.605911330049261
    • numErrors: 1353
    • numExamples: 2233
    • success: true
    • time: 0
  • predict:
    • success: true
    • time: 8
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • success: true
  • time: 8
• success: true

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