Recap from Day 011

In day 011 we explored Support Vector Machines(SVM) on a deeper level. We saw how SVM works under the hood, with loads of examples.

Today, we will start looking at Common Regression Algorithms.

Common Regression Algorithms.

Linear Regression

Linear regression is a statistical modeling technique used for finding linear relationship between target and one or more predictor variables.

There are two types of linear regression- Simple and Multiple.

“In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables.”

Source: MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf

Best Used…

• When you need an algorithm that is easy to interpret and fast to fit

• As a baseline for evaluating other, more complex, regression models

Nonlinear Regression

“Nonlinear regression is a regression in which the dependent or criterion variables are modeled as a non-linear function of model parameters and one or more independent variables.”

Models are called nonlinear regression because the relationships between the dependent and independent parameters are not linear.

Source: MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf

Best Used…

• When data has strong nonlinear trends and cannot be easily transformed into a linear space

• For fitting custom models to data

You made it to the end of day 012. 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

*http://www.statisticssolutions.com/regression-analysis-nonlinear-regression/*

MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf