The Close Relationship Between Applied Statistics and Machine Learning

Last Updated on August 8, 2019

The machine learning practitioner has a tradition of algorithms and a pragmatic focus on results and model skill above other concerns such as model interpretability.

Statisticians work on much the same type of modeling problems under the names of applied statistics and statistical learning. Coming from a mathematical background, they have more of a focus on the behavior of models and explainability of predictions.

The very close relationship between the two approaches to the same problem means that both fields have a lot to learn from each other. The statisticians need to consider algorithmic methods was called out in the classic “two cultures” paper. Machine learning practitioners must also take heed, keep an open mind, and learn both the terminology and relevant methods from applied statistics.

In this post, you will discover that machine learning and statistical learning are two closely related but different perspectives on the same problem.

After reading this post, you will know:

• “Machine learning” and “predictive modeling” are a computer science perspective on modeling data with a focus on algorithmic methods and model skill.
• “Statistics” and “statistical learning” are a mathematical perspective on modeling data with a focus
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