What is regression?
Regression is one of the main types of supervised learning in machine learning. In regression, the training set contains observations (also called features) and their associated continuous target values. The process of regression has two phases:
- The first phase is exploring the relationships between the observations and the targets. This is the training phase.
- The second phase is using the patterns from the first phase to generate the target for a future observation. This is the prediction phase.
The overall process is depicted in the following diagram:
Figure 5.1: Training and prediction phase in regression
The major difference between regression and classification is that the output values in regression are continuous, while in classification they are discrete. This leads to different application areas for these two supervised learning methods. Classification is basically used to determine desired memberships or characteristics...
