Regression Algorithms-2 Notes – Class 12 Data Science (844)
Regression Algorithms-2 Notes deals with Multiple Linear Regression and Non-Linear Regression. It is aligned with CBSE Class 12 DS Syllabus and pointwise notes.
Multiple Linear Regression
- Multiple Linear Regression is an extension of Linear Regression that uses multiple independent variables to predict the value of a dependent variable.
- For example, the cholesterol level of a person can be predicted using age, weight, and height as independent variables, while cholesterol level is the dependent variable.
- Unlike Simple Linear Regression, which uses only one independent variable, Multiple Linear Regression uses two or more independent variables, each having its own coefficient (slope).
- The basic equation of Multiple Linear Regression is:
Yi = β₀ + β₁Xi₁ + β₂Xi₂ + β₃Xi₃ + … + βₚXiₚ + ϵᵢ - In this equation, Yi represents the dependent variable, β₀ is the intercept, β₁, β₂, … βₚ are the coefficients of the independent variables, and ϵᵢ represents the error term.
Non-Linear Regression
- Non-Linear Regression is used when the relationship between the independent variable(s) and the dependent variable cannot be represented by a straight line.
- In Non-Linear Regression, the relationship is represented as y = f(x, β), where x is the independent variable, y is the dependent variable, and f(x, β) is a non-linear function.
- Examples of non-linear functions include exponential, logarithmic, trigonometric, and power functions.
- The graph of Linear Regression is a straight line, whereas the graph of Non-Linear Regression is a curve.
- For example, if a person’s income increases exponentially every month, Non-Linear Regression can be used to predict the income for the next month.
