Wrapper classifier that addresses incompatible training and test data by building a mapping between the training data that a classifier has been built with and the incoming test instances' structure. Model attributes that are not found in the incoming instances receive missing values, so do incoming nominal attribute values that the classifier has not seen before. A new classifier can be trained or an existing one loaded from a file.
The table below describes the options available for InputMappedClassifier.
The base classifier to be used.
Ignore case when matching attribute names and nomina values.
Set the path from which to load a model. Loading occurs when the first test instance is received. Environment variables can be used in the supplied path.
Don't output a report of model-to-input mappings.
Trim white space from each end of attribute names and nominal values before matching.
The table below describes the capabilities of InputMappedClassifier.
Numeric class, Nominal class, Missing class values, Date class, Binary class
Date attributes, Binary attributes, String attributes, Nominal attributes, Empty nominal attributes, Unary attributes, Numeric attributes, Missing values
Min # of instances