Note: this page autoupdates while a run is in progress
(see end of log file)
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Reading examples into memory...100..200..Features must be in increasing order!!!
LINE: +1 4:1 1:1 2:1 3:1
./run:17: Failed (RuntimeError)
=== END program1: ./run learn ../dataset2/train --- FAILED [0s]
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) svmlight-linear: SVMlight for binary classification using a linear kernel (http://svmlight.joachims.org)
(dataset:Dataset) father: Predict whether a pair of people have the father relation.
(stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
(evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).
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.