Note: this page autoupdates while a run is in progress
(see end of log file)
construct is not a supported option===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
=== START program2: ./run learn ../program1/data
error: memory exhausted or requested size too large for range of Octave's index type -- execution of ./run failed
=== END program2: ./run learn ../program1/data --- OK [2900s]
=== END program1: ./run learn ../dataset3/train --- OK [2902s]
===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
error: load: unable to find file model
error: called from:
error: ./run at line 2134, column 11
=== END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- FAILED [1s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- FAILED [1s]
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) binary-to-multi: Allows multiclass classification to be run on binary classification datasets (trivial reduction).
Go to the page for the run and look at the log file for signs of the responsible error.
You can also download the run and run it locally on your machine (a README file should
be included in the download which provides more information).
We said that a run was simply a program/dataset pair, but that's not the full story.
A run actually includes other helper programs such as the evaluation program and
various programs for reductions (e.g., one-versus-all, hyperparameter tuning).
More formally, a run is a given by a run specification,
which can be found on the page for any run.
A run specification is a tree where each internal node represents a program
and its children represents the arguments to be passed into its constructor.
For example, the one-versus-all program takes your binary classification program
as a constructor argument and behaves like a multiclass classification program.