Part 2 - Regression

Regression models (both linear and non-linear) are used for Predicting a Real Value, like salary for example. If your independent variable is time, then you are forecasting future values, otherwise your model is predicting present but unknown values.

Following are Machine Learning Regression models:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector for Regression (SVR)
• Decision Tree Classification
• Random Forest Classification

Note: Below are the Assumptions of Linear Regression (Simple/Multiple):

1. Linearity
2. Homoscedasticity
3. Multivariate normality
4. Independence of Errors
5. Lack of Multi-Collinearity

Hope this helps:

Arun Manglick