Conclusion

Statistical Model:

A mathematical process that attempts to describe the population from which a sample came, which allows us to make predictions of future samples from that population....

Machine Learning:

Machine Learning is the science that allows computers to learn and improve their learning over time, by feeding them data and information in the form of observations and real-world interactions....

Difference between Statistical Models and Machine Learning

The Difference between Statistical Models and Machine Learning are as follows:...

Similarities between the statistical model and machine learning:

In order to examine data and generate predictions, statistical modeling, and machine learning both require mathematical models. In order to recognize the underlying patterns and relationships in the data, they both involve fitting a model to the data. To accurately interpret the results and comprehend the model’s limits, both approaches call for a certain level of domain knowledge and data analytic abilities. Both methods rely on algorithms to process data and draw conclusions. Regression analysis, analysis of variance, and hypothesis testing are often used techniques in statistical modeling. Algorithms like decision trees, neural networks, and support vector machines are frequently employed in machine learning. The choice of acceptable features or variables to include in the model, as well as careful evaluation of the influence of outliers, missing data, and other data quality issues, are prerequisites for both statistical modeling and machine learning. To make sure the model is reliable and correct, both strategies entail model validation and evaluation. This covers methods including goodness-of-fit testing, residual analysis, and cross-validation....

Conclusion :

A statistical model makes a prediction based on the model’s assumptions after using the correlation or relationship between the variables. These models use mathematical equations to make predictions and have a clear understanding of how to interpret the parameters, which can aid in determining how the data relate to one another.On the flip hand, a machine learning model can be used to analyze a wide range of data types with complicated variable interactions. In order to make more accurate predictions, it also needs a lot of data. Since they are self-learners, they can draw knowledge from the past without being specifically trained....