Previous topic


Next topic

Generic MLProblems

This Page


Before a dataset can be fed to a Learner, it must first be converted into an MLProblem.

MLProblem objects are simply iterators with some extra properties. Hence, from an MLProblem, examples can be obtained by iterating over the MLProblem.

MLProblem objects also contain metadata, i.e. “data about the data”. For instance, the metadata could contain information about the size of the input or the set of all possible values for the target. The metadata (field metadata of an MLProblem) is represented by a dictionary mapping strings to arbitrary objects.

Finally, an MLProblem has a length, as defined by the output of __len__(self).

The mlproblems package is divided into different modules, based on the nature of the machine learning problem or task being implemented.

The modules are:

  • mlproblems.generic: MLProblems not specific to a particular task.
  • mlproblems.classification: classification MLProblems.
  • mlproblems.ranking: ranking MLProblems.

Follow the links below to learn more about these modules: