Systems and techniques for modeling and/or analyzing manufacturing processes are presented. A dataset component generates a plurality of binary classification datasets based on process data associated with one or more fabrication tools. A learning component generates a plurality of learned models based on the plurality of binary classification datasets and applies a weight to the plurality of learned models based on a number of data samples associated with the plurality of binary classification datasets to generate a weighted plurality of learned models. A merging component merges the weighted plurality of learned models to generate a process model for the process data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a memory storing computer executable components; and a processor configured to execute the following computer executable components stored in the memory: a dataset component that generates a plurality of binary classification datasets based on process data associated with one or more fabrication tools; a learning component that generates a plurality of learned models based on the plurality of binary classification datasets and applies a weight to the plurality of learned models based on a number of data samples associated with the plurality of binary classification datasets to generate a weighted plurality of learned models; and a merging component that merges the weighted plurality of learned models to generate a process model for the process data. 2. The system of claim 1, wherein the dataset component generates the plurality of binary classification datasets based on modified process data that includes replicated data. 3. The system of claim 1, wherein the dataset component generates the plurality of binary classification datasets based on modified process data that includes summarized data. 4. The system of claim 1, wherein the dataset component generates the plurality of binary classification datasets based on a data matrix that includes at least one of sensor measurement data, spectral data and classifier data. 5. The system of claim 1, wherein the dataset component generates the plurality of binary classification datasets according to a number of different classes included in the process data. 6. The system of claim 1, further comprising an output component that outputs the process model as a library of functions for predicting a classification of a unit of processing. 7. The system of claim 1, further comprising an output component that outputs the process model as a single function for predicting a classification of a unit of processing. 8. The system of claim 1, further comprising an analysis component that predicts a classification of a unit of processing based on the process model. 9. The system of claim 8, wherein the analysis component determines one or more input parameters associated with a particular classification for a unit of processing based on the process model. 10. The system of claim 9, wherein the analysis component ranks the one or more input parameters associated with the particular classification for the unit of processing. 11. A method, comprising: employing a processor that facilitates execution of computer executable instructions stored on a non-transitory computer readable medium to implement operations, comprising: receiving a dataset associated with process data for one or more fabrication tools; generating a plurality of classification datasets based on a number of classes included in the process data; generating one or more learned functions for each of the plurality of classification datasets; and combining the one or more learned functions for each of the plurality of classification datasets to generate a process model associated with the process data. 12. The method of claim 11, wherein the generating the plurality of classification datasets comprises generating the plurality of classification datasets based on modified process data that includes replicated data. 13. The method of claim 11, wherein the generating the plurality of classification datasets comprises generating the plurality of classification datasets based on modified process data that includes summarized data. 14. The method of claim 11, wherein the generating the plurality of classification datasets comprises generating the plurality of classification datasets based on a data matrix that includes at least one of sensor measurement data, spectral data and classifier data. 15. The method of claim 11, wherein the generating the plurality of classification datasets comprises generating a classification dataset for each of the different classes included in the process data. 16. The method of claim 11, further comprising: predicting a classification of a unit of processing based on the process model. 17. The method of claim 11, further comprising: determining one or more input parameters associated with a particular classification for a unit of processing based on the process model. 18. A computer-readable medium having stored thereon computer-executable instructions that, in response to execution by a system including a processor, cause the system to perform operations, the operations including: generating a plurality of binary classification datasets associated with a unique classification for a unit of processing based on process data; generating one more learned functions for each of the plurality of binary classification datasets; and generating a process model for the process data by merging the one more learned functions for each of the plurality of binary classification datasets. 19. The computer-readable medium claim 18, wherein the generating the plurality of binary classification datasets comprises generating the plurality of binary classification datasets based on a data matrix that includes at least one of sensor measurement data, spectral data and classifier data. 20. The computer-readable medium claim 18, wherein the generating the plurality of binary classification datasets comprises generating the plurality of binary classification datasets according to a number of different classes included in the process data.