MissingValueStrategy
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package model
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction. - nullPrediction: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, abort the scoring process and give no prediction. - defaultChild: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, evaluate the attribute defaultChild which gives the child to continue traversing with. Requires the presence of the attribute defaultChild in every non-leaf Node. - weightedConfidence: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, the confidences for each class is calculated from scoring it and each of its sibling Nodes in turn (excluding any siblings whose predicates evaluate to FALSE). The confidences returned for each class from each sibling Node that was scored are weighted by the proportion of the number of records in that Node, then summed to produce a total confidence for each class. The winner is the class with the highest confidence. Note that weightedConfidence should be applied recursively to deal with situations where several predicates within the tree evaluate to UNKNOWN during the scoring of a case. - aggregateNodes: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, we consider evaluation of the Node's predicate being TRUE and follow this Node. In addition, subsequent Nodes to the initial Node are evaluated as well. This procedure is applied recursively for each Node being evaluated until a leaf Node is reached. All leaf Nodes being reached by this procedure are aggregated such that for each value attribute of such a leaf Node's ScoreDistribution element the corresponding recordCount attribute values are accumulated. The value associated with the highest recordCount accumulated through this procedure is predicted. The basic idea of missingValueStrategy aggregateNodes is to aggregate all leaf Nodes which may be reached by a record with one or more missing values considering all possible values. Strategy aggregateNodes calculates a virtual Node and predicts a score according to this virtual Node. Requires the presence of attribute recordCount in all ScoreDistribution elements. - none: Comparisons with missing values other than checks for missing values always evaluate to FALSE. If no rule fires, then use the noTrueChildStrategy to decide on a result. This option requires that missing values be handled after all rules at the Node have been evaluated. Note: In contrast to lastPrediction, evaluation is carried on instead of stopping immediately upon first discovery of a Node who's predicate value cannot be determined due to missing values.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction. - nullPrediction: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, abort the scoring process and give no prediction. - defaultChild: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, evaluate the attribute defaultChild which gives the child to continue traversing with. Requires the presence of the attribute defaultChild in every non-leaf Node. - weightedConfidence: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, the confidences for each class is calculated from scoring it and each of its sibling Nodes in turn (excluding any siblings whose predicates evaluate to FALSE). The confidences returned for each class from each sibling Node that was scored are weighted by the proportion of the number of records in that Node, then summed to produce a total confidence for each class. The winner is the class with the highest confidence. Note that weightedConfidence should be applied recursively to deal with situations where several predicates within the tree evaluate to UNKNOWN during the scoring of a case. - aggregateNodes: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, we consider evaluation of the Node's predicate being TRUE and follow this Node. In addition, subsequent Nodes to the initial Node are evaluated as well. This procedure is applied recursively for each Node being evaluated until a leaf Node is reached. All leaf Nodes being reached by this procedure are aggregated such that for each value attribute of such a leaf Node's ScoreDistribution element the corresponding recordCount attribute values are accumulated. The value associated with the highest recordCount accumulated through this procedure is predicted. The basic idea of missingValueStrategy aggregateNodes is to aggregate all leaf Nodes which may be reached by a record with one or more missing values considering all possible values. Strategy aggregateNodes calculates a virtual Node and predicts a score according to this virtual Node. Requires the presence of attribute recordCount in all ScoreDistribution elements. - none: Comparisons with missing values other than checks for missing values always evaluate to FALSE. If no rule fires, then use the noTrueChildStrategy to decide on a result. This option requires that missing values be handled after all rules at the Node have been evaluated. Note: In contrast to lastPrediction, evaluation is carried on instead of stopping immediately upon first discovery of a Node who's predicate value cannot be determined due to missing values.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction. - nullPrediction: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, abort the scoring process and give no prediction. - defaultChild: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, evaluate the attribute defaultChild which gives the child to continue traversing with. Requires the presence of the attribute defaultChild in every non-leaf Node. - weightedConfidence: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, the confidences for each class is calculated from scoring it and each of its sibling Nodes in turn (excluding any siblings whose predicates evaluate to FALSE). The confidences returned for each class from each sibling Node that was scored are weighted by the proportion of the number of records in that Node, then summed to produce a total confidence for each class. The winner is the class with the highest confidence. Note that weightedConfidence should be applied recursively to deal with situations where several predicates within the tree evaluate to UNKNOWN during the scoring of a case. - aggregateNodes: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, we consider evaluation of the Node's predicate being TRUE and follow this Node. In addition, subsequent Nodes to the initial Node are evaluated as well. This procedure is applied recursively for each Node being evaluated until a leaf Node is reached. All leaf Nodes being reached by this procedure are aggregated such that for each value attribute of such a leaf Node's ScoreDistribution element the corresponding recordCount attribute values are accumulated. The value associated with the highest recordCount accumulated through this procedure is predicted. The basic idea of missingValueStrategy aggregateNodes is to aggregate all leaf Nodes which may be reached by a record with one or more missing values considering all possible values. Strategy aggregateNodes calculates a virtual Node and predicts a score according to this virtual Node. Requires the presence of attribute recordCount in all ScoreDistribution elements. - none: Comparisons with missing values other than checks for missing values always evaluate to FALSE. If no rule fires, then use the noTrueChildStrategy to decide on a result. This option requires that missing values be handled after all rules at the Node have been evaluated. Note: In contrast to lastPrediction, evaluation is carried on instead of stopping immediately upon first discovery of a Node who's predicate value cannot be determined due to missing values.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction. - nullPrediction: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, abort the scoring process and give no prediction. - defaultChild: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, evaluate the attribute defaultChild which gives the child to continue traversing with. Requires the presence of the attribute defaultChild in every non-leaf Node. - weightedConfidence: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, the confidences for each class is calculated from scoring it and each of its sibling Nodes in turn (excluding any siblings whose predicates evaluate to FALSE). The confidences returned for each class from each sibling Node that was scored are weighted by the proportion of the number of records in that Node, then summed to produce a total confidence for each class. The winner is the class with the highest confidence. Note that weightedConfidence should be applied recursively to deal with situations where several predicates within the tree evaluate to UNKNOWN during the scoring of a case. - aggregateNodes: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, we consider evaluation of the Node's predicate being TRUE and follow this Node. In addition, subsequent Nodes to the initial Node are evaluated as well. This procedure is applied recursively for each Node being evaluated until a leaf Node is reached. All leaf Nodes being reached by this procedure are aggregated such that for each value attribute of such a leaf Node's ScoreDistribution element the corresponding recordCount attribute values are accumulated. The value associated with the highest recordCount accumulated through this procedure is predicted. The basic idea of missingValueStrategy aggregateNodes is to aggregate all leaf Nodes which may be reached by a record with one or more missing values considering all possible values. Strategy aggregateNodes calculates a virtual Node and predicts a score according to this virtual Node. Requires the presence of attribute recordCount in all ScoreDistribution elements. - none: Comparisons with missing values other than checks for missing values always evaluate to FALSE. If no rule fires, then use the noTrueChildStrategy to decide on a result. This option requires that missing values be handled after all rules at the Node have been evaluated. Note: In contrast to lastPrediction, evaluation is carried on instead of stopping immediately upon first discovery of a Node who's predicate value cannot be determined due to missing values.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction. - nullPrediction: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, abort the scoring process and give no prediction. - defaultChild: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, evaluate the attribute defaultChild which gives the child to continue traversing with. Requires the presence of the attribute defaultChild in every non-leaf Node. - weightedConfidence: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, the confidences for each class is calculated from scoring it and each of its sibling Nodes in turn (excluding any siblings whose predicates evaluate to FALSE). The confidences returned for each class from each sibling Node that was scored are weighted by the proportion of the number of records in that Node, then summed to produce a total confidence for each class. The winner is the class with the highest confidence. Note that weightedConfidence should be applied recursively to deal with situations where several predicates within the tree evaluate to UNKNOWN during the scoring of a case. - aggregateNodes: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, we consider evaluation of the Node's predicate being TRUE and follow this Node. In addition, subsequent Nodes to the initial Node are evaluated as well. This procedure is applied recursively for each Node being evaluated until a leaf Node is reached. All leaf Nodes being reached by this procedure are aggregated such that for each value attribute of such a leaf Node's ScoreDistribution element the corresponding recordCount attribute values are accumulated. The value associated with the highest recordCount accumulated through this procedure is predicted. The basic idea of missingValueStrategy aggregateNodes is to aggregate all leaf Nodes which may be reached by a record with one or more missing values considering all possible values. Strategy aggregateNodes calculates a virtual Node and predicts a score according to this virtual Node. Requires the presence of attribute recordCount in all ScoreDistribution elements. - none: Comparisons with missing values other than checks for missing values always evaluate to FALSE. If no rule fires, then use the noTrueChildStrategy to decide on a result. This option requires that missing values be handled after all rules at the Node have been evaluated. Note: In contrast to lastPrediction, evaluation is carried on instead of stopping immediately upon first discovery of a Node who's predicate value cannot be determined due to missing values.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction.
- lastPrediction: If a Node's predicate evaluates to UNKNOWN while traversing the tree, evaluation is stopped and the current winner is returned as the final prediction. - nullPrediction: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, abort the scoring process and give no prediction. - defaultChild: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, evaluate the attribute defaultChild which gives the child to continue traversing with. Requires the presence of the attribute defaultChild in every non-leaf Node. - weightedConfidence: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, the confidences for each class is calculated from scoring it and each of its sibling Nodes in turn (excluding any siblings whose predicates evaluate to FALSE). The confidences returned for each class from each sibling Node that was scored are weighted by the proportion of the number of records in that Node, then summed to produce a total confidence for each class. The winner is the class with the highest confidence. Note that weightedConfidence should be applied recursively to deal with situations where several predicates within the tree evaluate to UNKNOWN during the scoring of a case. - aggregateNodes: If a Node's predicate value evaluates to UNKNOWN while traversing the tree, we consider evaluation of the Node's predicate being TRUE and follow this Node. In addition, subsequent Nodes to the initial Node are evaluated as well. This procedure is applied recursively for each Node being evaluated until a leaf Node is reached. All leaf Nodes being reached by this procedure are aggregated such that for each value attribute of such a leaf Node's ScoreDistribution element the corresponding recordCount attribute values are accumulated. The value associated with the highest recordCount accumulated through this procedure is predicted. The basic idea of missingValueStrategy aggregateNodes is to aggregate all leaf Nodes which may be reached by a record with one or more missing values considering all possible values. Strategy aggregateNodes calculates a virtual Node and predicts a score according to this virtual Node. Requires the presence of attribute recordCount in all ScoreDistribution elements. - none: Comparisons with missing values other than checks for missing values always evaluate to FALSE. If no rule fires, then use the noTrueChildStrategy to decide on a result. This option requires that missing values be handled after all rules at the Node have been evaluated. Note: In contrast to lastPrediction, evaluation is carried on instead of stopping immediately upon first discovery of a Node who's predicate value cannot be determined due to missing values.
Defines a strategy for dealing with missing values.