From the course: Machine Learning and AI Foundations: Classification Modeling

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One task, many algorithms

One task, many algorithms

From the course: Machine Learning and AI Foundations: Classification Modeling

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One task, many algorithms

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- [Instructor] In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems. In this course, the class of problems is binary classification, and there's a remarkable diversity of approaches. I've always been a fan of Tom Khabaza's 9 Laws of Data Mining, a topic that I discuss at length in my essential elements course. The fourth law, no free lunch for the data miner, states the following. "The right model for a given application can only be discovered by experiment." So when playing this game, trial and error is going to be our method. It's at the core of doing binary classification well. This is really the premise of the course. Competency at binary classification equals, competency at all of the multiple algorithms that are capable of binary classification. And in addition, you have to become competent at using these multiple approaches, both individually and together. In a book called The Master Algorithm, Pedro Demingos…

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