MLcomp version alpha Statistics: 1403 users, 461 datasets, 360 programs, 16577 runs (0 queued, 0 running), 0 workers

Run 14659

Creator	internal
Program	RBFNetwork_weka_numeric
Dataset	simple-regression-test
Task type	Regression
Created	28d17h ago
Download	Login required!

Status: Done!

Total Time	2m58s
Max Memory Usage	418M
Domain	Regression
Learn time	1m36s
Train MSE	157
Predict train time	1m25s
Test MSE	151
Predict test time	1m21s

Log file

Note: ./weka/classifiers/functions/LinearRegression.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
Regression
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Regression
=== END program1: ./run learn ../dataset2/train --- OK [96s]

===== 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 [9s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Regression
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [85s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [21s]

===== 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 [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Regression
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [81s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	4m57.851s
user	2m40.654s
sys	2m15.936s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) RBFNetwork_weka_numeric: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/functions/RBFNetwork.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance.
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    
  • (dataset:Dataset) simple-regression-test: a linear function of one variable
  • (stripper:Program[Strip]) regression-utils: Validates and inspects a dataset in Regression format.
  • (evaluator:Program[Evaluate]) regression-evaluator: Evaluates predictions of Regression datasets (continuous outputs).

Results

• doTest:
  • evaluate:
    • meanSquaredError: 151.380183398873
    • numExamples: 10000
    • success: true
    • time: 0
    • totalMeanSquaredError: 1513801.83398873
  • predict:
    • success: true
    • time: 81
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • meanSquaredError: 157.036733338587
    • numExamples: 300000
    • success: true
    • time: 21
    • totalMeanSquaredError: 47111020.001576
  • predict:
    • success: true
    • time: 85
  • strip:
    • success: true
    • time: 9
• exitCode: 0
• learn:
  • success: true
  • time: 96
• success: true

Comments: