Many students get confused between machine learning and statistical modelling as this both are related to each other. Let’s point out main differences between Machine learning and statistical modelling. According to history statistics was originated in 17th century and Machine learning has invented long time after statistics was born, in 1959.
We can organize main differences between statistical modelling and machine learning as below.
| Machine learning | Statistical Modelling |
| Subfield of computer science and Artificial Intelligence | Subfield of mathematics |
| Uses algorithms for predictions | Uses formalization of relationships between variables in the form of mathematical equations to predict outcomes |
| Can deal with large amount of data and attributes | Mostly deals with small amounts of data and attributes (Possibility of over-fitting) |
| Require less human effort as more workload do by machine | More human effort- Modeler has to understand the relation and the implementation that a variable has on an equation |
| Uses fewer Assumptions | Uses more mathematical based assumptions |
| Strong Predictive ability and high accuracies(Strong predictive power asmachine is ‘fit’ and ‘trained’ to find patterns of a data set.) | Best Estimates |
However, according to many researchers and scientists Machine learning and statistical modelling can be defined as,
Machine Learning is,
an algorithm that can learn from data without relying on rules-based programming.
Statistical Modelling is,
formalization of relationships between variables in the form of mathematical equations.