Methods, systems, and apparatuses for performing target parameter analysis for an assembly line including a plurality of stations. One method includes receiving, with an electronic processor, training data associated with the assembly line. The training data including a plurality of attributes. The method also includes receiving, with the electronic processor, value addition data for each of the plurality of stations. The value addition data for each of the plurality of stations specifying a non-negative value added by each of the plurality of stations. The method also includes learning, with the electronic processor, a decision tree based on the training data and the value addition data. The method also includes performing the target parameter analysis based on the decision tree.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of performing target parameter analysis for an assembly line including a plurality of stations, the method comprising: receiving, with an electronic processor, training data associated with the assembly line, the training data including a plurality of attributes; receiving, with the electronic processor, value addition data for each of the plurality of stations, the value addition data for each of the plurality of stations specifying a non-negative value added by each of the plurality of stations; learning, with the electronic processor, a decision tree based on the training data and the value addition data; and performing the target parameter analysis based on the decision tree. 2. The method of claim 1, wherein learning the decision tree includes: determining a weight of each attribute included in the plurality of attributes based on the value addition data; and for each attribute and for each decision node included in the decision tree until a stopping criteria for splitting is reached, determining a best split and a quality measure of the best split based on a decision theoretic measure, multiplying the quality measure of the best split with the weight of the attribute to generate a weighted quality measure, and determining the attribute included in the plurality of attributes with the best split based on the weight quality measure. 3. The method of claim 2, wherein determining the weight of each attribute includes determining a weight of each attribute based on a cost of performing one or more tests at one of the plurality of stations and a user-specified, non-negative parameter controlling an influence of the cost on the decision tree. 4. The method of claim 2, wherein determining the weight of each attribute includes determining a weight of each attribute within a range of 0 to 1. 5. The method of claim 2, wherein determining the best split includes determining a best split using an iterative dichotomiser algorithm. 6. The method of claim 2, wherein determining the quality measure of the best split based on the decision theoretic measure includes determining the quality measure of the best split based on information gain. 7. The method of claim 1, wherein performing the target parameter analysis includes predicting a value of a target parameter for incoming data. 8. The method of claim 7, wherein predicting the value of the target parameter includes predicting a value of scrap. 9. The method of claim 7, wherein predicting the value of the target parameter includes predicting a value of yield. 10. The method of claim 1, wherein performing the target parameter analysis includes determining an attribute affecting scrap or affecting yield. 11. The method of claim 1, wherein performing the target parameter analysis includes determining at least one station included in the plurality of stations affecting scrap or affecting yield. 12. The method of claim 1, further comprising evaluating the decision tree using a test set. 13. A system for performing target parameter analysis for an assembly line including a plurality of stations, the system comprising: a server including an electronic processor, the electronic processor configured to receive training data associated with the assembly line, the training data including a plurality of attributes, receive value addition data for each of the plurality of stations, the value addition data for each of the plurality of stations specifying a non-negative value added by each of the plurality of stations, learn a decision tree based on the training data and the value addition data, and provide a suggested value for a tunable parameter associated with the assembly line based on the decision tree. 14. The system of claim 13, wherein the electronic processor is configured to learn the decision tree by determining a weight of each attribute included in the plurality of attributes based on the value addition data; and for each attribute and for each decision node included in the decision tree until a stopping criteria for splitting is reached, determining a best split and a quality measure of the best split based on a decision theoretic measure, multiplying the quality measure of the best split with the weight of the attribute to generate a weighted quality measure, and determining the attribute included in the plurality of attributes with the best split based on the weight quality measure. 15. The system of claim 13, wherein the tunable parameter includes a tunable parameter for at least one of the plurality of stations. 16. A computer readable medium including instructions that, when executed by an electronic processor, cause the electronic processor to a set of functions, the set of functions comprising: receiving training data associated with an assembly line including a plurality of stations, the training data including a plurality of attributes; receiving value addition data for each of the plurality of stations, the value addition data for each of the plurality of stations specifying a non-negative value added by each of the plurality of stations; generating a data model based on the training data and the value addition data; and providing an output evaluating the assembly line based on the data model. 17. The computer readable medium of claim 16, wherein the data model includes a decision tree. 18. The computer readable medium of claim 16, wherein the output includes an identifier of at least one station included in the plurality of stations. 19. The computer readable medium of claim 16, wherein the output includes an ordered list of stations included in the plurality of stations. 20. The computer readable medium of claim 16, wherein the output includes a root cause analysis, wherein an importance of the root cause analysis is dependent on a location of the root cause within the assembly line.