classification models can be further broken down on the type of output variables and the number of output variables produced by them this nomenclature is extremely important to understand the type of classification problem you are dealing with by looking at the dataset attributes and the objective to be solved binary classificationwhen we have total of two categories to differentiate between in the output response variable in the datathen the problem is termed as binary classification problem hence you would need an appropriate model that performs binary classification (known as binary classification modela popular binary classification problem is the "email classification problemin this problemthe candidate -mails need to be classified and labeled into either of the two different categories"spamor "non spam(also known as "ham"multi-class classificationthis is an extension of the binary classification problem in this case we have more than two categories or classes into which our data can be classified an example of the multi-class classification problem is predicting handwritten digits where response variable can have any value ranging from to this becomes -class classification problem the multi-class classification is tough problem to solve and the general scheme for solving the multi-class problem mostly involves some modifications of the binary classification problem multi-label classificationthese classification problems typically involve data where the output variable is not always single value but vector having multiple values or labels simple example is predicting categories of news articles that can have multiple labels for each news article like sciencepoliticsreligionand so on classification models often output the actual class labels or probabilities for each possible class label that gives confidence level for each class in the prediction the following are the major output formats from classification models category classification outputin some classification modelsthe output for any unknown data point is the predicted category or class label these models usually calculate the probabilities of all the categoriesbut report only one class label having the maximum probability or confidence category probability classification outputin these classification modelsthe output is the probability value of each possible class label these models are important when we want to further use the output produced by our classification model for detailed analysis or to make complex decisions very simple example can be typical marketing candidate selection problem in this problemby getting the probability output of potential conversionwe can narrow down our marketing expenses regression models in classification modelswe saw that the output variable predicted by the model was discrete valueeven when we got the output as probability valuethose probability values were tied to the discrete class label values of the possible categories regression models are another subset of the supervised learning family of models in these modelsthe input data is generally labeled with real valued output variable (continuous instead of discreteregression analysis is an important part of statistical learning and it has very similar utilityin the field of machine learning in statistical learningregression analysis is used to find relationships between the dependent and the independent variables (which can be one or more than onein the case of regression modelswhen we feed our new data points to our learned\trained regression modelthe output of the model is continuous value