Difference between Statistical Model and Machine Learning

In this article, we are going to see the difference between statistical model and machine learning

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.

Examples: Hypothesis testing, Correlation, etc.

Some problem statements solved by statistical modeling:

• employing inferential statistics to calculate the average income of a population from a random sample
•  estimating a stock’s future price using previous data, and time series analysis.

Objectives of Statistical Model:

• used for proving any result such as hypothesis testing, and p-value.
• search data for interesting information (exploratory) such as generating hypotheses.
• building a protective model.

Assumptions in Statistical Model:

• Independence, states that there shouldn’t be any relationships between the observations in the collection.
• Normality requires that the response variable’s distribution is approximately normal, with data symmetric around the mean.
• Linearity indicates that the relationship between the response variable and predictor variable(s) should be linear. 
• No multicollinearity, suggesting the independence of predictor variables from each other.
• outliers, the dataset should not contain any outliers that may influence the results.

Types of Statistical Models

1.  The group of probability distributions that have a finite number of parameters is known as parametric.
2.  Nonparametric models are those where the kind and quantity of parameters are adjustable and not predetermined.
3.  Semiparametric means that the parameter has both a parametric and a non-parametric.

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.

According to Arthur Samuel machine learning is,  “the field of study that gives computers the ability to learn without being explicitly programmed “ i.

                                                                   OR

According to Tom Mitchell, “Machine learning is the study of computer algorithms that allow computer programs to improve through experience automatically”.

Example: Predicting house price with the help of a machine learning model on the basis of attributes such as location, and area by the help of machine learning we can find out the relationship between the dependent variable (i.e house price) on independent features (i.e location, area, year of formation) and we can predict the price of another input on the resulting relation.

Some problem statements for machine learning :

• Recommendation: Utilize collaborative filtering to suggest movies to viewers based on their prior viewing habits and ratings.
• Diseases Prediction: employing a support vector machine to make a prediction about a patient’s propensity to develop a specific disease based on their medical history and genetic information.

Assumptions in Machine Learning:

• Data is independent and identically distributed (IID), which means that every data point is independent of the others and has the same distribution.
• The assumption that there is a linear relationship between the input variables and the output variable underlies some models, such as linear regression.
• Normality, Some models presuppose that the model’s input variables and/or error terms are distributed normally.
• No multicollinearity, Linear models presuppose that the input variables are not highly associated with one another and do not exhibit multicollinearity.
• High Sample Size, Certain models rely on the sample size being sufficiently big to guarantee precise parameter estimates.

Model Comparison

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.

In conclusion, both statistical and machine learning models can produce outcomes that are more accurate in a variety of circumstances. The approach we use should be determined by the issue we’re attempting to resolve in the algorithm.