| Classification | Regression |
| – Classification is the task of predicting a discrete class label. – In a classification problem data is labelled into one of two or more classes. – A classification problem with two classes is called binary, more than two classes is called a multi-class classification. – Classifying an email as spam or non-spam is an example of a classification problem. | – Regression is the task of predicting a continuous quantity. – A regression problem requires the prediction of a quantity. – A regression problem with multiple input variables is called a multivariate regression problem. – Predicting the price of a stock over a period of time is a regression problem. |
| Classification | Regression | |
| Value takes by target variable | Discrete set of values | Continuous Values |
| How to Assess Model Fit | Percentage of correct classficication | Root mean squared error |
| Type of Response Variable | Categorical (Categorical Data = Nominal = String = Qualitative Data = Ordinal = Booleon) | Continous |
| Predict | Pass or Fail/ Spam or Not Spam | Percentage / Price of Stock Market |
| Dependant Variable | Categorical | Numerical |
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