When solving statistical problems, you may have to choose between using classification vs regression analysis. Both of these statistical methods are useful for different kinds of data and different types of problems.
Knowing the key differences between these two analytical tools will help you select the correct one for your problem. Both classification and regression analysis are supervised learning (or predictive) methods used in statistics and machine learning.
They are also called “causal” models because they help identify the causes of a particular outcome or label. Classification is used when we want to know to what category something belongs (e.g., red apple, green apple, yellow apple, etc.). It tells us how likely something is to belong to a particular category based on training data.
Therefore, it can help predict whether some other object belongs to that category or not. On the other hand, regression analysis is used when we want to know how something changes in response to some other variable (e.g., price of iPhone X changes with storage size).
It tells us how predictable something is as a result of another variable and can help predict future values of that same variable given known initial values.(END)
What is Classification Analysis?
Classification analysis is a statistical method used for predicting categorical variables, such as what species of animal some plant is, what type of plant some animal is, and so on. It can also be used to predict missing values when the rest of the data is available.
This is commonly referred to as imputation. Classification analysis can be applied when you want to predict what category or label an observation or sample belongs to. This label is often the result of a question that you want answered, such as: What species is this animal? What type of plant is this? Is this part defective?
What is the price of this product? What is the risk associated with this loan? What is the probability that this insurance claim is fraudulent?
What is Regression Analysis?
Regression analysis is a statistical method used for predicting numerical variables, such as how much some property (such as price) changes with the change in some other number (such as size).
It can also be used to predict missing values when the rest of the data is available. It can be applied when you want to predict future numerical values (y) given past values (x) and other known variables (x).
This is most often the result of a question that you want answered, such as: How much will a car of given size and make cost in a given city?
What will happen to the price of crude oil in the next month given that hurricane season has started? How much will a person weigh given that she has a certain height? How much will a product cost given its size, material, and production cost?
Differences Between Classification and Regression
Most importantly, the data for regression analysis is continuous data, whereas classification analysis is applied to categorical data. – Data for regression analysis is continuous data and can be easily represented using a graph.
On the other hand, data for classification analysis is categorical and can be represented using a tree diagram. – Regression analysis deals with the relationship between two or more numerical variables.
Classification analysis deals with how likely an observation (also known as an instance) belongs to a particular class or category.
This is known as the dependent variable. – The predictions made by regression analysis are continuous values or numbers.
The predictions made by classification analysis are categorical or discrete values. – Regression analysis predicts how the dependent variable (y) changes as a result of changes in the independent variable (x). Classification analysis predicts the category to which an observation belongs given the values of the independent variable(s).
There are many similarities between regression and classification analysis, but there are also many differences.
The most important difference is that the data for regression analysis is continuous data, whereas classification analysis is applied to categorical data.
Both classification and regression analysis are supervised learning methods used in statistics and machine learning. They are also called “causal” models because they help identify the causes of a particular outcome or label.