Regression vs Classification in Machine Learning: Understanding the Difference

The most notable difference between regression and segmentation is that while retreat helps to predict continuous value, division predicts different class labels. There is also a discrepancy between the two types of machine learning algorithms.

The regression algorithm can predict a discrete value that is in the form of a whole value
The segmentation algorithm can predict a continuous value if it is in the probability position of a category label
Let us consider a database that contains student information for a particular university. The retrospective algorithm can be used in this case to predict the height of any student depending on their weight, gender, diet, or major subject. We use retreat in this case because the height is a continuous mass. There is an infinite number of possible values of human height.

Conversely, a split can be used to analyze whether an email is spam or not. The algorithm checks keywords in the email and sender address to determine if the email may be spam. Similarly, while the regression model can be used to predict the next day's temperature, we can use a separation algorithm to determine whether it will be cold or hot depending on the given temperature values.