REGRESSION.pptx(unit4).pptx

Regression and it types

1. REGRESSION

2. MEANING Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).

3. A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables. A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables.

4. EXAMPLE we can say that age and height can be described using a linear regression model. Since a person’s height increases as age increases, they have a linear relationship.

5. TYPES OF REGRESSION Linear Regression Logistic Regression

6. LINEAR REGRESSION The simplest case of linear regression is to find a relationship using a linear model (i.e. line) between an input-independent variable (input single feature) and an output-dependent variable. This is called Bivariate Linear Regression. FORMULA:- Y = m * x + b

7. LOGICAL REGRESSION It is used when the output is categorical. It is more like a classification problem. The output can be Success / Failure, Yes / No, True/ False, or 0/1. If the output has only two possibilities, it is called Binary Logistic Regression.

8. EXAMPLE FOR LOGICAL REGRESSION:- For example, you want to create a model identifying whether the breast cancer is malignant(1) or benign(0). For example, if you want to classify if the input email is spam(1) or not (0).

9. FORMULA FOR REGRESSION