tive class and the other class being the negative class herepositive doesn' represent having benefit or valuebut rather what the object of the study is sowhen looking for spam"positivecould mean the spam class which of the two classes is called positive is often subjective matterand specific to the domain the iris exampleon the other handis an example of multiclass classification problem another example is predicting what language website is in from the text on the website the classes here would be pre-defined list of possible languages for regression tasksthe goal is to predict continuous numberor floating-point number in programming terms (or real number in mathematical termspredicting person' annual income from their educationtheir ageand where they live is an example of regression task when predicting incomethe predicted value is an amountand can be any number in given range another example of regression task is predicting the yield of corn farm given attributes such as previous yieldsweatherand number of employees working on the farm the yield again can be an arbitrary number an easy way to distinguish between classification and regression tasks is to ask whether there is some kind of continuity in the output if there is continuity between possible outcomesthen the problem is regression problem think about predicting annual income there is clear continuity in the output whether person makes $ , or $ , year does not make tangible differenceeven though these are different amounts of moneyif our algorithm predicts $ , or $ , when it should have predicted $ , we don' mind that much by contrastfor the task of recognizing the language of website (which is classification problem)there is no matter of degree website is in one languageor it is in another there is no continuity between languagesand there is no language that is between english and french generalizationoverfittingand underfitting in supervised learningwe want to build model on the training data and then be able to make accurate predictions on newunseen data that has the same characteristics as the training set that we used if model is able to make accurate predictions on unseen datawe say it is able to generalize from the training set to the test set we want to build model that is able to generalize as accurately as possible we ask linguists to excuse the simplified presentation of languages as distinct and fixed entities supervised learning