Examples of Evaluation

• com.mobixess.jodb.soda.api.Evaluation
  for implementation of callback evaluations.
  
  To constrain a {@link Query} node with your own callbackEvaluation, construct an object that implements the Evaluation interface and register it by passing it to {@link Query#constrain(Object) Query#constrain(Object)}.
  
  Evaluations are called as the last step during query execution, after all other constraints have been applied. Evaluations in higher level {@link Query} nodes in the query graph are called first.use {@code com.mobixess.jodb.query.api.Evaluation} instead
• cross.reputation.model.Evaluation
• edu.stanford.nlp.sempre.Evaluation
  An Evaluation measures how well the system is doing on a set of examples. Formally, it is just a collection of arbitrary statistics. @author Percy Liang
• net.sf.mp.demo.conference.domain.conference.Evaluation

  Title: Evaluation

  Description: Domain Object describing a Evaluation entity

• ognl.Evaluation
  An Evaluation is and object that holds a node being evaluated and the source from which that node will take extract its value. It refers to child evaluations that occur as a result of the nodes' evaluation.
• org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation
  Weight matrix of the residuals between model and observations.
  Immutable class. @version $Id: Weight.java 1416643 2012-12-03 19:37:14Z tn $ @since 3.1
• org.fenixedu.academic.domain.Evaluation
• org.fnlp.ml.eval.Evaluation
  评测类 给定数据集，计算分类器的分类性能。 @author xpqiu
• org.neo4j.graphdb.traversal.Evaluation
• org.ocpsoft.rewrite.bind.Evaluation
  @author Lincoln Baxter, III
• org.terrier.evaluation.Evaluation
  An abstract class for evaluating the retrieval results. @author Gianni Amati, Ben He, Vassilis Plachouras
• teammates.jdo.Evaluation
  Evaluation is a persistent data class that holds information pertaining to an evaluation on Teammates. @author Gerald GOH
• teammates.testing.object.Evaluation
• tv.floe.metronome.eval.Evaluation
• types.Evaluation
• weka.classifiers.Evaluation
  Class for evaluating machine learning models.

  -------------------------------------------------------------------

  General options when evaluating a learning scheme from the command-line:

  -t filename
  Name of the file with the training data. (required)

  -T filename
  Name of the file with the test data. If missing a cross-validation is performed.

  -c index
  Index of the class attribute (1, 2, ...; default: last).

  -x number
  The number of folds for the cross-validation (default: 10).

  -no-cv
  No cross validation. If no test file is provided, no evaluation is done.

  -split-percentage percentage
  Sets the percentage for the train/test set split, e.g., 66.

  -preserve-order
  Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').

  -s seed
  Random number seed for the cross-validation and percentage split (default: 1).

  -m filename
  The name of a file containing a cost matrix.

  -l filename
  Loads classifier from the given file. In case the filename ends with ".xml", a PMML file is loaded or, if that fails, options are loaded from XML.

  -d filename
  Saves classifier built from the training data into the given file. In case the filename ends with ".xml" the options are saved XML, not the model.

  -v
  Outputs no statistics for the training data.

  -o
  Outputs statistics only, not the classifier.

  -i
  Outputs information-retrieval statistics per class.

  -k
  Outputs information-theoretic statistics.

  -classifications "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
  Uses the specified class for generating the classification output. E.g.: weka.classifiers.evaluation.output.prediction.PlainText or : weka.classifiers.evaluation.output.prediction.CSV -p range
  Outputs predictions for test instances (or the train instances if no test instances provided and -no-cv is used), along with the attributes in the specified range (and nothing else). Use '-p 0' if no attributes are desired.

  Deprecated: use "-classifications ..." instead.

  -distribution
  Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).

  Deprecated: use "-classifications ..." instead.

  -r
  Outputs cumulative margin distribution (and nothing else).

  -g
  Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).

  -xml filename | xml-string
  Retrieves the options from the XML-data instead of the command line.

  -threshold-file file
  The file to save the threshold data to. The format is determined by the extensions, e.g., '.arff' for ARFF format or '.csv' for CSV.

  -threshold-label label
  The class label to determine the threshold data for (default is the first label)

  -------------------------------------------------------------------

  Example usage as the main of a classifier (called FunkyClassifier):

   public static void main(String [] args) { runClassifier(new FunkyClassifier(), args); } 

  ------------------------------------------------------------------

  Example usage from within an application:

   Instances trainInstances = ... instances got from somewhere Instances testInstances = ... instances got from somewhere Classifier scheme = ... scheme got from somewhere Evaluation evaluation = new Evaluation(trainInstances); evaluation.evaluateModel(scheme, testInstances); System.out.println(evaluation.toSummaryString()); 

  @author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 7228 $

Examples of teammates.testing.object.Evaluation

Examples of teammates.testing.object.Evaluation

Examples of teammates.testing.object.Evaluation

Examples of tv.floe.metronome.eval.Evaluation

Examples of types.Evaluation

Examples of weka.classifiers.Evaluation

Examples of weka.classifiers.Evaluation

Examples of weka.classifiers.Evaluation

Examples of weka.classifiers.Evaluation

Examples of weka.classifiers.Evaluation