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Parameter
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Explanation
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Intervals
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Number of bins used to discretize a continuous output
Note: Only valid for regression tasks More Info |
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Anti COD
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Reduce the curse of dimensionality (COD) effect in high dimensional input spaces
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Eta
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Sets a threshold for the hitrate of a rule to pass the merge test. A hitrate of 100% is required, if this value is set to 0. If you chose an value of 1, then the required hitrate is 50%.
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Kernel width
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Controls the k-nearest-neighbor component of the prediction mechanism for classification tasks. Set it to 1, to follow a pure 1-nearest-rule strategy.
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Sigma
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Controls the k-nearest-neighbor component of the prediction mechanism for regression tasks
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Min. rule mass
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Threshold for the mass of a rule. All rules with a lower mass are reduced. The mass of a rule is the number of learn data tuples merged within the equivalent cluster. Rules with a low mass have weak statistical support. More Info
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Kill small rules
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If you enable this option, then all rules with a mass less than Min. rule mass are deleted permanently from the model. Thus, later on when using the model, it will have no effect if you chose a smaller value for Min. rule mass.
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Prune rules
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Enable the context-sensitive feature selection which individually eliminates irrelevant inputs from each rule. You should always enable this option!
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Prune anyway
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If you enable this option, then the calculations necessary for the context-sensitive feature selection are executed even if pruning is disabled, to allow you, to enable this option later on when using the model.
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Use Weights
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Use feature weights calculated on the basis of the transinformation of the particular input to the output
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Euclidean
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Use Euclidean or Manhattan City-Block distance
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| Hint: | It is always a good idea to use the default values. You have to press the Defaults button after changing the output column to re-calculate appropriate values.
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Note: The parameters can only be edited if the check box Skip Tuning is checked.
More Info