Gradient Boosting Machine (GBM) modeling is a powerful machine learning technique for advanced root cause analysis in manufacturing. It will uncover problems that would be missed by regression-based statistical modelling techniques and single tree methods, but can easily be used by analysts with no expertise in statistics and modelling to solve complex problems. It is an excellent choice for advanced equipment commonality analysis and will detect interactions between process factors (for example, machines, recipes, process dates) that are responsible for bad product. It can also be used to identify complex nonlinear relationships and interactions between product quality measurements (for example, yield, defects, field returns) and upstream measurements from the product, process, equipment, component, material, or environment.
Published By: Forcepoint
Published Date: Jun 06, 2019
In a recent study, IDC analyzed the business value and benefits of supporting network operations with Forcepoint NGFW solutions. The research included interviews with eight organizations relying upon Forcepoint’s NGFW to connect and protect their networks and business operations.
IDC created a model based upon their analysis to identify the costs and real benefits of deploying Forcepoint NGFW. The highlights of this analytical model are captured in their Business Value Snapshot; and the complete results are available for download now in the IDC Quantifying the Operational and Security Results of Switching to Forcepoint NGFW Whitepaper.
Download the Snapshot now
Download the IDC Whitepaper now to learn how the eight organizations surveyed realized improved efficiency, availability and security with Forcepoint NGFW, as well as a return of cost in only seven months and an average 5-year ROI of 510%.
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