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## Difference Between Classification and Regression

Classification vs Regression

Classification and regression are learning techniques to create models of prediction from gathered data. Both techniques are graphically presented as classification and regression trees, or rather flowcharts with divisions of data after every step, or rather, “branch” in the tree. This process is called recursive partitioning.

Classification

Classification is a technique used to arrive at a schematic that shows the organization of data starting with a precursor variable. The dependent variables are what classify the data into groups. The classification tree starts with the independent variable, which branches out into two groups as determined by the existing dependent variables. It is meant to elucidate the responses in the form of categorization brought about by the dependent variables.

Regression

Regression is a prediction method that is based on an assumed or known numerical output value. This output value is the result of a series of recursive partitioning, with every step having one numerical value and another group of dependent variables which branch out to another pair such as this. The regression tree starts with one or more precursor variables, and terminates with one final output variable. The dependent variables are either continuous or discrete numerical variables.

What is the difference between Classification and Regression

The main difference between the classification tree and the regression tree is their dependent variable. For the classification tree, the dependent variables are categorical, while the regression tree has numerical dependent variables. Those of the classification tree also have a set amount of unordered values, while those of the regression tree have either discrete yet ordered values or indiscrete values. A regression tree is constructed with the purpose of fitting a regression system to each determinant branch in a way that the expected output value comes up. On the other hand, a classification tree branches out as determined by a dependent variable derived from the previous node.

Regression and classification trees are helpful techniques to map out the process that points to a studied outcome, whether in classification or a single numerical value.

 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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