# The Role of Statistics in Machine Learning

Some sites online would have you believe that statistics and machine learning are two completely different technologies. For example, when you read *Statistics vs. Machine Learning, fight!*, you get the idea that the two technologies are not only different, but downright hostile toward each other. The fact is that statistics and machine learning have a lot in common and that statistics represents one of the five *tribes* (schools of thought) that make machine learning feasible. The five tribes are

**Symbolists:**The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.**Connectionists:**The origin of this tribe is in neuroscience. This group relies on backpropagation to solve problems.**Evolutionaries:**The origin of this tribe is in evolutionary biology. This group relies on genetic programming to solve problems.**Bayesians:**This origin of this tribe is in statistics. This group relies on probabilistic inference to solve problems.**Analogizers:**The origin of this tribe is in psychology. This group relies on kernel machines to solve problems.

The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm (the *master algorithm*) that can learn anything. Of course, achieving that goal is a long way off. Even so, scientists such as Pedro Domingos are currently working toward that goal.

Using the Bayesian tribe strategy, you solve most problems using some form of statistical analysis. You do see strategies embraced by other tribes described, but the main reason you begin with statistics is that the technology is already well established and understood. In fact, many elements of statistics qualify more as *engineering* (in which theories are implemented) than *science* (in which theories are created). Understanding the role of algorithms in machine learning is essential to defining how machine learning works.