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THis is more machine learning questions, but perhaps someone will be able to help. I would like to know what is the diference between regression and classification when we try to generate output for a training data set x?

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5 Answers 5

up vote 18 down vote accepted

Regression: the output variable takes continuous values.

Classification: the output variable takes class labels.

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The output variable (I assume you mean response variable) can also be boolean with logistic regression right? –  Justin Bozonier Jul 24 '13 at 2:06
    
Binary logistic regression also outputs a continuous variable. Specifically, the regression estimates the odds that a variable is in a given class as a function of the predictor variables. –  James Thompson Jul 29 '13 at 16:45
    
Binary Logistic regression produces a continuous output but not to try to give a continuous output at the data (regression) but in order to classify them in two classes –  K. Stasko Mar 8 at 13:05

Same as Tim said a different way. Regression involves estimating or predicting a response. Classification is identifying group membership.

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signed up to up vote this response. i love the plain English description. –  Julian Apr 26 '13 at 5:49

f: x-> y

here,

if y is discrete/categorical variable, then classification problem

if y is real number/continuous, then regression problem.

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This seems to simply reiterate answers from long ago. If you have something new to add, please clarify your answer. –  robjohn Jul 27 at 12:51

http://www.differencebetween.com/difference-between-classification-and-vs-regression/

In brief: • Classification trees have dependent variables that are categorical and unordered. • Regression trees have dependent variables that are continuous values or ordered whole values.

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Regression means to predict the output value using training data. Classification means to group the output into a class. e.g. we use regression to predict the house price from training data and use classification to predict the type of tumor i.e. harmful or not harmful using training data.

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