Classification vs Regression in Machine Learning

The most common problems solved by Machine Learning algorithms are classification and regression. It is very important to know which problem you are facing. Fundamentally, classification is about predicting a label and regression is about predicting a quantity.

Both problems are based on the idea of predictive modeling. We want to build a model using historical data to make a prediction on new data. The type of prediction that the model gives depends on whether it is a classification or regression problem.

Classification Problems

Classification predictive modeling is the task of defining a mapping function which maps input variables to discrete output variables. The output variables are often called labels or categories. The input variables can be both discrete and continuous, unlike the output variables which must be discrete. A common example of a classification problem is classifying emails as spam or non-spam.

It is also possible that a classification model gives the output as the probability of belonging to each category. These probabilities can then be converted into a certain category by choosing…