“Many of my candidates argue that predictive modeling and machine learning are the same thing,” said Data Science and Analytics Executive Recruiter, Nancy Darian. Relevant articles though seem to say while there are key similarities between them they are really two distinct elements of artificial intelligence.
Definitions
An article from Educba.com said “Predictive modeling is a subset and an application of machine learning.” Another article from ITChronicles.com said, “Predictive analytics often uses a machine-learning algorithm.” Those two definitions seem to be at odds with each other. There is agreement, however, that predictive modeling is a statistical process and machine learning is a computational one.
Machine learning is defined as an area of computer science which uses cognitive learning methods to program systems without the need of being explicitly programmed. Predictive modeling is a mathematical technique which uses statistics for prediction. Predictive modeling can use machine learning as a tool but machine learning does not always focus on prediction.
Key Similarities
Both applications require larges sets of data and analyze patterns to determine future outcomes. “They both look back on the past in order to understand the future and are widely used in many of the same industries like finance and retail. They allow the consolidation of technology to create easier end-user processes, they automate all kinds of practices saving companies time and money, they help understand consumer behavior and enhance economic and supply chain indicators,” according to ITChronicles.com. Clearly these applications provide a competitive advantage to those companies that use them. The growth in the Data Scientist role and increasing use of artificial intelligence are prime indicators of their value.
Key Distinctions
Machine learning is adaptive with the systems adapting and learning, while predictive modeling guesses at the probability of an outcome given a specific set of data. In machine learning the statistical model is updated automatically while in predictive modeling the model often has to be run manually multiple times. Machine learning is data driven while predictive modeling is use case driven. Machine learning has the drawback of needing training data to be created before the algorithm can be put to use, while predictive modeling gets better the more historical data is used yet sometimes fails to take in specific set of parameters in real time.
Facing these questions in your own research? Interested in discussing opportunities in the data science marketplace further? Contact Smith Hanley Associates’ Data Science and Analytics Executive Recruiter, Nancy Darian at ndarian@smithhanley.com.
