Machine learning uses cognitive learning methods for programming systems. On the other hand, Predictive Modeling in Nashville makes predictions using statistics. Keep reading to understand the differences between the two concepts.
1. Modus Operandi
Machine learning revolves around adaptive techniques. The systems can adapt and learn whenever a new set of data is added. It does not require to be programmed directly.
Predictive modeling is based on using classifiers and detection theory. It can guess the probability of an outcome after being provided with an input data set.
2. Applications
Machine learning has a lot of applications in bioinformatics. It is also applied in the interfaces of brain machines. Moreover, machine learning can classify DNA sequences. It also plays a crucial role in computational anatomy and computer vision. The part of machine learning in detecting internet fraud is unparalleled. Machine learning in Nashville can also help in identifying credit card frauds.
On the contrary, predictive modeling is used in the case of archaeology. Customer relationship management benefits from predictive modeling. Predictive modeling is also used in the field of healthcare. Many companies use predictive modeling to optimize their marketing campaigns. Furthermore, predictive monitoring can be used for crisis management and disaster management.
Summing Up
Machine learning is data-driven, while predictive modeling is case-driven. Both technologies are providing practical solutions to organizations all over the world. Top organizations are investing in these technologies to tackle real-world problems. Decide what’s best for your business and invest in it.
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