AI_Part_3_Regression vs Classification Models

Regression vs. Classification in Machine Learning: What’s the Difference?

Comparing regression vs classification in machine learning is very confusing at times. This can eventually make it difficult to implement the right methodologies for solving prediction problems. Both regression and classification are types of supervised machine learning algorithms, where a model is trained according to the existing model along with correctly labeled data. We will understand the difference between regression and classification algorithms.

What is Regression Machine Learning??

Regression algorithms predict a continuous value based on the input variables. The main goal of regression problems is to estimate a mapping function based on the input and output variables. If your target variable is a quantity like income, scores, height or weight, or the probability of a binary category (like the probability of rain in particular regions), then you should use the regression model.

The different types of regression algorithms include:

1. Simple linear regression

With simple linear regression, you can estimate the relationship between one independent variable and another dependent variable using a straight line, given both variables are quantitative.

2. Multiple linear regression

An extension of simple linear regression, multiple regression can predict the values of a dependent variable based on the values of two or more independent variables.

3. Polynomial regression

The main aim of polynomial regression is to model or find a nonlinear relationship between dependent and independent variables.

What is Classification Machine Learning?

Classification is a predictive model that approximates a mapping function from input variables to identify discrete output variables, which can be labels or categories. The mapping function of classification algorithms is responsible for predicting the label or category of the given input variables.?A classification algorithm can have both discrete and real-valued variables, but it requires that the examples be classified into one of two or more classes.?

The different types of classification algorithms include:

1. Decision tree classification

In this algorithm, a classification model is created by building a decision tree where every node of the tree is a test case for an attribute and each branch coming from the node is a possible value for that attribute.

2. Random forest classification

This tree-based algorithm includes a set of decision trees that are randomly selected from a subset of the main training set. The random forest classification algorithm aggregates outputs from all the different decision trees to decide on the final output prediction, which is more accurate than any of the individual trees.

3. K-nearest neighbor

The K-nearest neighbor algorithm assumes that similar things exist in close proximity to each other. It uses feature similarity for predicting the values of new data points. The algorithm helps group similar data points together according to their proximity. The main goal of the algorithm is to determine how likely it is for a data point to be a part of a specific group.

Regression vs Classification in Machine Learning: Understanding the Difference

The most significant difference between regression and classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning algorithms.

• A regression algorithm can predict a discrete value which is in the form of an integer quantity
• A classification algorithm can predict a continuous value if it is in the form of a class label probability

Let’s consider a dataset that contains student information about a particular university. A regression algorithm can be used in this case to predict the height of any student based on their weight, gender, diet, or subject major. We use regression in this case because height is a continuous quantity. There is an infinite number of possible values for a person’s height.

On the contrary, classification can be used to analyze whether an email is spam or not spam. The algorithm checks the keywords in an email and the sender’s address to find out the probability of the email being spam. Similarly, while a regression model can be used to predict temperature for the next day, we can use a classification algorithm to determine whether it will be cold or hot according to the given temperature values.