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Machine Learning Vs Statistical Modelling

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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 IntelligenceSubfield of mathematics
Uses algorithms for predictionsUses formalization of relationships between variables in the form of mathematical equations to predict outcomes
Can deal with large amount of data and attributesMostly deals with small amounts of data and attributes (Possibility of over-fitting)
Require less human effort as more workload do by machineMore human effort- Modeler has to understand the relation and the implementation that a variable has on an equation
Uses fewer AssumptionsUses 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.

Muthu is a talented graduate from the University of Cambridge. Her major is Data Science (Big Data) and Machine Learning. She is also an experienced software engineering professional with a lot of innovative ideas and a demonstrated history of working in the information technology and services industry.

Machine Learning Vs Statistical Modelling

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