Recap from Day 013

In day 013 we continued with Common Regression Algorithms, touching Gaussian Process Regression and SVM Regression.

Common Regression Algorithms Continued

Regression Tree

“A regression tree is built through a process known as binary recursive partitioning, which is an iterative process that splits the data into partitions or branches, and then continues splitting each partition into smaller groups as the method moves up each branch.”

Decision trees for regression are similar to decision trees for classification, the difference is that they are modified to be able to predict continuous responses.

Source: MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf

Best Used…

• When predictors are categorical (discrete) or behave nonlinearly

Generalized Linear Model

“A generalized linear model is a special case of nonlinear models that uses linear methods. It involves fitting a linear combination of the inputs to a nonlinear function (the link function) of the outputs.”

Source: MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf

Best Used…

• When the response variables have nonnormal distributions, such as a response variable that is always expected to be positive

You made it to the end of day 014. I hope you found this informative. Thank you for taking time out of your schedule and allowing me to be your guide on this journey.

Reference

MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf