Machine learning involves the use of multiple types of learning algorithms, including regression and classification algorithms. Before we understand the major differences between regression and classification algorithms, we need to understand what each of them exactly is. Regression algorithms are used to estimate the mapping function from input variables to continuous (numerical) output variables. In simpler terms, regression models are used to approximate the value of an input variable on a linear scale. For example, regression algorithms can be used to determine the cost of a home, based on location, size, etc. Classification algorithms, on the other hand, are used to estimate the mapping function from input variables to discrete (class labels) output variables. Basically, classification algorithms are used to place objects into distinguishable classes. For example, a classification algorithm can be used to determine whether the given home will be affordable or not, based on the price. The differences between regression and classification are explained in detail below.
Differences between regression and classification algorithms
Depending upon the type of output required, either regression or classification algorithms can be used in machine learning programs.
Regression algorithms
Regression algorithms are used to establish a relationship between the dependent and independent variables. It involves the prediction of continuous values for the given data rather than dividing it into classes. It can also be used to identify the distribution movement of the input data based on historical data. For example, a regression algorithm can be used to identify the probability of rain by analyzing the given data. It doesn’t give a clear answer, whether it will rain or not. It just predicts the probability of rain.
Some of the types of regression algorithms are:
Linear regression: Linear regression predicts the dependent variable value based on a given independent variable. It finds out a linear relationship between input and output.
Random forest: Random forest is suitable for large datasets with missing values. The algorithm constructs multiple numbers of decision trees to predict data values.
Classification algorithms
Classification algorithms are used to categorize inputs into predefined classes. It involves the prediction of discrete values for the given data. A classification model can be illustrated by “IF-THEN” rules. An object can either be classified into one class or another with distinguished sorting. For example, a classification algorithm can be used to identify whether a region will receive rainfall or not. It gives a clear answer to the outcome of the event based on the input data.
Some of the types of classification algorithms used in machine learning are:
KNN algorithms: K-nearest-neighbor is one of the most popular classification algorithms used in machine learning. It takes a bunch of input points and uses them to learn to label other points closest to the available points(nearest neighbors).
Decision trees: Decision trees build a classification model in the form of a tree. It continuously breaks the data into smaller subsets while simultaneously developing an associated decision tree.
Linear classifiers: The Naive Bayes Classifier is the most popular type of linear classifier algorithm used in classification. A Naive Bayes Classifier assumes that the presence of a particular feature is unique in a class. It is easy to build and useful for large datasets.
Selecting the right algorithm based on the type of problem is crucial for the success of the machine learning program. Businesses looking to implement machine learning must understand the objective of the machine learning model and decide whether to use regression or classification algorithms. They can collaborate with AI solutions providers and ask them to program the algorithm accordingly, once they have a clear idea about the differences between regression and classification algorithms.
