Classification:

A classification problem is when the output variable is a category or a group, such as “black” or “white” or “spam” and “no spam”. This is a type of problem where we predict the categorical response value where the data can be separated into specific “classes” (ex: we predict one of the values in a set of values).

Some examples are :

• this mail is spam or not?

• will it rain today or not?

• is this picture a cat or not?

Basically ‘Yes/No’ type questions called binary classification.

Other examples are :

• this mail is spam or important or promotion?

• is this picture a cat or a dog or a tiger?

This type is called multi-class classification.

Regression:

A regression problem is when the output variable is a real value, such as “Rupees” or “height.” This is a type of problem where we need to predict the continuous-response value (ex : above we predict number which can vary from -infinity to +infinity) Some examples are • what is the price of house in a specific city? • what is the value of the stock? • how many total runs can be on board in a cricket game? etc… there are tons of things we can predict if we wish.