Regression Algorithm

Regression models are used to predict a continuous value. They are a type of Supervised Learning. Beginning with the simple case, Single Variable Linear Regression is a technique used to model the relationship between a single input independent variable (feature variable) and an output dependent variable using a linear model i.e a line. The more general case is Multi Variable Linear Regression where a model is created for the relationship between multiple independent input variables (feature variables) and an output dependent variable. The model remains linear in that the output is a linear combination of the input variables.

There is a third most general case called Polynomial Regression where the model now becomes a non-linear combination of the feature variables i.e there can be exponential variables, sine and cosine, etc. This however requires knowledge of how the data relates to the output. Regression models can be trained using Stochastic Gradient Descent (SGD).

This image is an example of the regression model.

The various types of regression are as follows:

1)Linear Regression

2)Logistic Regression

3)Polynomial Regression

4)Stepwise Regression

5}Ridge Regression

6)Lasso Regression

7)ElasticNet Regression