# .css-4zleql{display:block;}std::steam  # 100 Days Of ML Code — Day 012

Jehoshaphat I. Abu
·Jul 21, 2018·

## 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

MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf