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To this end, simple attribute types were introduced, which can be used to still identify which attributes are nominal or ordinal etc. Added interface for learning algorithms which is generic in the input and output space as well as the type of config with which the respective algorithm can be configured. Maybe the name is still not as appropriate.
https://github.com/fmohr/AILibs.git into general/improvement/outsourcedinterfaces Conflicts: JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/core/dataset/Dataset.java JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/core/dataset/attribute/CategoricalAttribute.java JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/core/evaluation/evaluator/MonteCarloCrossValidationEvaluator.java JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/functionprediction/learner/learningcurveextrapolation/LearningCurveExtrapolator.java softwareconfiguration/mlplan/src/main/java/ai/libs/mlplan/metamining/MetaMLPlan.java
https://github.com/fmohr/AILibs.git into general/improvement/outsourcedinterfaces Conflicts: JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/classification/singlelabel/loss/PrecisionAsLoss.java JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/regression/loss/MeanSquaredError.java JAICore/jaicore-ml/src/main/java/ai/libs/jaicore/ml/regression/loss/RootMeanSquaredError.java
* Refactored dataset and ARFFDatasetAdapter. * Old dataset stuff which is not yet working was moved to ai.libs.jaicore.ml.core.olddataset * Unit Tests for ARFFDatasetAdapter.
resolved some bugs, and added a util method to convert a regression dataset into a classification dataset. This can come in handy if, for example, on openml.org, a dataset is defined as numeric by mistake
https://github.com/fmohr/AILibs.git into general/improvement/outsourcedinterfaces Conflicts: JAICore/jaicore-experiments/src/main/java/ai/libs/jaicore/experiments/databasehandle/ExperimenterRestSQLHandle.java JAICore/jaicore-search/src/main/java/ai/libs/jaicore/search/algorithms/standard/bestfirst/BestFirst.java
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