Classification vs Regression in Machine Learning

1. Classification vs Regression in Machine Learning nixustechnologies

3. In machine learning, predictions based on labeled datasets are done using either regression or classification algorithms. Regression and classification where on one hand belongs to the same category of machine learning techniques (i.e supervised learning) but on the other hand both these terms differ with respect to the types of problems these are used in. Regression is used for problems with continuous forms of data such as salary, marks, age, etc. while classification algorithms are used for data with categories or discrete values such as Men or Women, Yes or No, Good or Bad, etc.

5. Regression in Machine Learning Regression algorithms are used to find the correlations between the data i.e dependent variables and independent variables. The input for a process being evaluated is independent variables(x) (also known as Features). The dependent variables(y) are the process’s output. Regression is used as a predictive model to evaluate the outcome of continuous order. In regression, the major task is to map the input value (x) to the continuous output variable (y). 1. Simple Linear Regression: A mapping function is used in simple linear regression to model the linear relationship between an independent variable and a dependent variable that must be predicted. Consider, for illustration, that a locality’s housing costs are only influenced by its geography. So, basing the trained model on historical data, one may forecast housing costs given the area of any new region.

6. Regression in Machine Learning 2. Multiple Linear Regression: Regression of this kind involves establishing a connection between a number of independent variables and a dependent variable. For instance, a restaurant’s evaluations are influenced by the caliber of the food along with the ambiance, site, and services. Therefore, in this situation, several independent variables influence the dependent parameter linearly (restaurant’s rating). 3. Polynomial Regression: The non-linear correlation between the independent and dependent variables is mapped by this algorithm. The mapping has a nonlinear equation since it uses several powers of an independent variable. This kind of algorithm, for instance, can be used to forecast Covid-19 cases. Polynomial regression can map the non-linearity and forecast since the growth or reduction in cases is not linearly connected to the number of persons wearing masks.

7. Regression  Classification The task of predicting a continuous quantity is known as regression. The classification process involves anticipating a discrete class label. The task is to map the input value (x) to the continuous output variable (y). The task is to map the input value(x) to the discrete output variable (y). A regression model can predict a discrete value, but only in the form of an integer quantity. A classification model can predict a continuous value, but it is in the form of a probability for a class label. The output variable in regression must be of continuous value. The output variable in Classification must be discrete.

8. Regression algorithms can be used to resolve other regression issues, such as weather prediction, age prediction, market movements, and more. Some uses of classification algorithms include speech recognition, email spam detection, and plant disease categorization. Three categories of regression are- simple linear regression, multiple linear regression, and polynomial regression. Three categories of classification involve- binary classification, multi-class classification, and multi- label classification.

9. Conclusion: So, we went over some of the concepts underlying Regression and Classification. We have also discussed the major differences of these algorithms. We’ve seen how these two algorithms are used in real-world situations. nixustechnologies