MLcomp version alpha Statistics: 2045 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 15442

Creator	internal
Program	LWL_weka_numeric
Dataset	housePricesFromZillow
Task type	Regression
Created	1y306d ago
Download	Login required!

Status: Done!

Total Time	39m27s
Max Memory Usage	418M
Domain	Regression
Learn time
Train MSE	121371674270
Predict train time
Test MSE	116672307289
Predict test time

Log file

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 [2s]

===== 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
Regression
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1660s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== 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 [705s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	39m32.780s
user	38m42.805s
sys	0m6.960s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) LWL_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/lazy/LWL.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler. Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression). For more info, see Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    
  • (dataset:Dataset) housePricesFromZillow: price beds baths sqft lot age property.tax
  • (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: 116672307289.467
    • numExamples: 3574
    • success: true
    • time: 0
    • totalMeanSquaredError: 4.16986826252556e+14
  • predict:
    • success: true
    • time: 705
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • meanSquaredError: 121371674269.798
    • numExamples: 8339
    • success: true
    • time: 1
    • totalMeanSquaredError: 1.01211839173584e+15
  • predict:
    • success: true
    • time: 1660
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • success: true
  • time: 2
• success: true

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