Regression is about finding an optimal function for identifying the data of continuous real values and make predictions of that quantity. If there are more than two classes, then it can be called a multi-class classification algorithm. Classification algorithm classifies the required data set into one or more labels an algorithm that deals with two classes or categories is known as a binary classifier. Classification is all about predicting a label or category. Let us discuss some key differences between Regression vs Classification in the following points: Key differences between Regression and Classification Hadoop, Data Science, Statistics & others Head to Head Comparison between Regression and Classification (Infographics)īelow is the Top 5 Comparison between Regression vs Classification : Classification is an algorithm in supervised machine learning that is trained to identify categories and predict in which category they fall for new values. Regression is an algorithm in supervised machine learning that can be trained to predict real number outputs. 
In advance, to differentiate between Classification and Regression, let us understand what does this terminology means in Machine Learning. In supervised machine learning, we have a known output value in the data set, and we train the model based on these and use it for prediction, whereas in unsupervised machine learning, we don’t have a known set of output values.
Machine Learning is broadly divided into two types they are Supervised machine learning and Unsupervised machine learning. In this article, Regression vs Classification, let us discuss the key differences between Regression and Classification.
Difference Between Regression and Classification
0 kommentar(er)
