=== START program2: ./run inspect ../dataset1/train
=== END program2: ./run inspect ../dataset1/train --- OK [1s]
=== START program2: ./run inspect ../dataset1/test
=== END program2: ./run inspect ../dataset1/test --- OK [1s]
real 0m4.384s
user 0m2.728s
sys 0m0.952s
Run specification
supervised-learning-processor: Main entry point for validating and inspecting a dataset for supervised learning datasets.
(Dataset) CollaborativeFilteringData: Runs data for CollaborativeFiltering domain. Rows are datasets and Columns are programs, with values as error rates of program running on dataset.
(Program[Inspect]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
(Program[Split]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
When you generate a run, you can set a time limit for the run (no more than 24 hours). After that point, we will terminate the program.
Your program can use 1.5GB of memory. More information here.
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.
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