Simple regression: Several good techniques can fit a line or a polynomial to a set of data points. Minimizing the square of the distance is a common technique. Once this line is drawn, a threshold may be set and the possible outcomes from classification are mapped to portions of the line.

Logistic regression: This also uses curve fitting techniques but with more complex curves, often sigmoid functions. The large jump in the sigmoid can be adjusted to provide a good threshold between the classification options.

Bayesian: Another option is to use bell curves, often called Bayesian functions, to match the data. This works well for clusters. Several bell curves can fit several different clusters and the best threshold can be set by their intersections.

Support vector machines: This is similar to fitting a line but extends it into multiple dimensions. A plane or collection of planes is positioned to maximize the distance from all the points. These planes become the threshold separating the space.

Decision tree: Some problems are complex enough that a single regression or threshold isn’t effective. A decision tree creates a flowchart or tree with multiple decisions at each step. In many cases, different variables are used at each step. The process is best for complex datasets where different variables behave very differently, such as when some variables are Boolean and others numerical.

Random forest: Finding the best collection of decisions for the best tree can be difficult because the possible options increase quickly with the complexity of the data set. The random forest builds many potential trees and tests them all.

Nearest neighbor: Instead of cutting up a data set with lines or planes, the nearest neighbor approach looks for definitive points in the space. New data points are classified by finding the nearest definitive point in the space. In some cases, the algorithms find a set of weights for the various data fields to adjust how the distance is calculated.

Neural networks: These are more elaborate AI algorithms that simulate collections of neurons that are arranged in a network. Each neuron can make a simple decision based upon its inputs. The decisions flow through the network until a final classification is made.

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