Machine Learning vs. Statistics
Original Blog Post: Machine Learning vs. Statistics
Authors: Tom Fawcett and Drew Hardin
Published: 2018-02-05
My summary:
This post is authored by a machine learning practitioner (Tom Fawcett) and a statistician (Drew Hardin), so they bring their unique perspectives and draw parallels and highlight differences between how ML practitioners and statisticians approach problems.
Here are some highlights.
Statistics
In statistics, the goal of modeling is approximating and then understanding the data-generating process, with the goal of answering the question you actually care about.
[T]he Statistician is concerned primarily with model validity, accurate estimation of model parameters, and inference from the model. However, prediction of unseen data points, a major concern of Machine Learning, is less of a concern to the statistician. Statisticians have the techniques to do prediction, but these are just special cases of inference in general.
Machine Learning
In Machine Learning, the predominant task is predictive modeling: the creation of models for the purpose of predicting labels of new examples.
The model does not represent a belief about or a commitment to the data generation process. Its purpose is purely functional.
ML practitioners are freed from worrying about difficult cases where assumptions are violated, yet the model may work anyway.
Here is the link again to their blog post for more details: Machine Learning vs. Statistics