both the casesthe output class is scalar value pointing to one specific class multi-label classification tasks are such that based on any input data samplethe output response is usually vector having one or more than one output class label simple real-world problem would be trying to predict the category of news article that could have multiple output classes like newsfinancepoliticsand so on popular classification algorithms include logistic regressionsupport vector machinesneural networksensembles like random forests and gradient boostingk-nearest neighborsdecision treesand many more regression machine learning tasks where the main objective is value estimation can be termed as regression tasks regression based methods are trained on input data samples having output responses that are continuous numeric values unlike classificationwhere we have discrete categories or classes regression models make use of input data attributes or features (also called explanatory or independent variablesand their corresponding continuous numeric output values (also called as responsedependentor outcome variableto learn specific relationships and associations between the inputs and their corresponding outputs with this knowledgeit can predict output responses for newunseen data instances similar to classification but with continuous numeric outputs one of the most common real-world examples of regression is prediction of house prices you can build simple regression model to predict house prices based on data pertaining to land plot areas in square feet figure - shows two possible regression models based on different methods to predict house prices based on plot area figure - supervised learningregression models for house price prediction the basic idea here is that we try to determine if there is any relationship or association between the data feature plot area and the outcome variablewhich is the house price and is what we want to predict thus once we learn this trend or relationship depicted in figure - we can predict house prices in the future for any given plot of land if you have noticed the figure closelywe depicted two types of models on purpose to show that there can be multiple ways to build model on your training data the main objective is to minimize errors during training and validating the model so that it generalized welldoes not overfit or get biased only to the training data and performs well in future predictions simple linear regression models try to model relationships on data with one feature or explanatory variable and single response variable where the objective is to predict methods like ordinary least squares (olsare typically used to get the best linear fit during model training