LF_EvaluateRegression Processing Resource

The regression evaluation PR allows you to obtain a fast root mean squared error (RMSE) score for a regression problem. This is a helpful place to start in tuning your choice of algorithm, features and parameters. The model isn’t saved when you use the evaluation PR. This PR has no init time parameters. Here are the runtime parameters:

• algorithmJavaClass - advanced—allows user to specify an algorithm on the basis of its Java class name
• algorithmParameters - parameters influencing the algorithm, documented either in the library’s own documentation or in AlgorithmParameters
• evaluationMethod—Cross - validation or hold-out?
• featureSpecURL - The xml file containing the feature specification
• inputASName - Input annotation set containing attributes/class
• instanceType - Annotation type to use as instance
• numberOfFolds - For cross-validation, how many folds to split the data into? Ten is common. Higher numbers give a better result, but the evaluation takes longer to run.
• numberOfRepeats - For hold-out evaluation, the evaluation can be repeated across randomized splits and averaged for a more accurate result.
• scaleFeatures - Use a feature scaling method?
• targetFeature - which feature on the instance annotation indicates the class?
• trainingAlgorithm - Which algorithm to use
• trainingFraction - For hold-out evaluation; in splitting off a training portion, what fraction should it be?

For an example feature specification, see the regression training PR documentation.